Table of Contents
Introduction
1. Tableau
2. LookerML
3. Qlik Sense
4. Klipfolio
5. Microsoft Power BI
Conclusion

Introduction

In the data-driven world, data visualization is the ultimate BI tool that takes large datasets from numerous sources, aiding data visualization engineers to analyze data and visualize it into actionable insights. In the data analysis process, data visualization is the final chapter that includes a variety of graphs, charts, and histograms in the form of reports and dashboards to make the data more friendly and understandable. 

Therefore, to create a data analysis report that stands out, AITechPark has accumulated the top five most popular data visualization tools. These data visualization tools will assist data visualization engineers, further help businesses understand their needs, and provide real-time solutions to streamline the business process.

1. Tableau

Tableau is one of the most popular data visualization tools used by data scientists and analysts to create customized charts and complex visualizations. The users can connect the data sources, which include databases, spreadsheets, cloud services, and other big data references, allowing them to import and transform data for their analysis. However, Tableau is not the right tool for data creation and preprocessing, as it does not support spreadsheet tools for multi-layered operations. Tableau is expensive when compared to other data visualization tools on the market. The cost of Tableau subscriptions varies; for instance, Tableau Public and Tableau Reader are free, while Tableau Desktop is available for $70/user/month, Tableau Explorer for $42/user/month, and Tableau Viewer for $15/user/month.

2. LookerML

LookerML is a powerful tool that helps data teams visualize capabilities and data inputs and create a powerful modeling layer that allows them to turn SQL into object-oriented code. To keep the workflow up and running without any challenges, teams can take advantage of Looker Blocks, a robust library of analytics code. However, beginners will still need some apprenticeship to learn the art of data visualization before working with Looker, as it provides complicated tools that might be difficult to understand at first glance. The tool also comes with pre-defined built-in visualizations that have some fixed standards and specifications, giving limited options for customization. The pricing varies from $5,000 per month to $7,000 per month, depending on the size and usage of the tool. 

3. Qlik Sense

Qlik Sense is a one-stop data visualization platform for data teams that provides an associative data analytics engine with a sophisticated AI system and a scalable multi-cloud architecture to deploy a mixture of SaaS, private, and on-premises cloud. The data team can combine, visualize, explore, and load datasets on Qlik Sense to create data charts, tables, and visualizations, further instantly updating itself according to the new data context. However, Qlik Sense has some major drawbacks, such as having fewer collaboration features, which are not sufficient for data visual engineers to perform tasks when compared to other competitors’ tools. On a trial basis, Qlik Sense Business is free for 30 days, and then it moves to paid versions that vary from $20 per month per user to $2700 per month for unlimited basic users. 

4. Klipfolio

Klipfolio is one of the data visualization tools in Canada as it allows data visualization engineers to access their data from multiple sources, such as databases, files, and web service applications, as connectors. The tool allows users to create custom drag-and-drop data visualizations where they can choose from different options like charts, graphs, scatter plots, etc. Klipfolo also creates KPI-based dashboards that enable companies to get a glimpse of their business performance. However, the weakness of the tool is that it only functions online and gets disrupted when the internet connection is unstable. Klipfolio also has a limited variety of data sources when compared to other data visualization tools on our list. In terms of pricing, Klipfolio offers a free trial of 14 days, followed by $49 per month for the basic business plan.

5. Microsoft Power BI

Microsoft’s Power BI is an easy-to-use data visualization tool that is available both for deployment and on-premise installation on the cloud infrastructure. The tool is complete within itself, as it supports a myriad of backend databases, such as Teradata, Salesforce, PostgreSQL, Oracle, Google Analytics, Github, Adobe Analytics, Azure, SQL Server, and Excel. According to users, Power BI tends to be praised for its ability to flow data and its modeling capabilities, making it one of the strong contenders in the data modeling and infrastructure markets. However, Power BI lacks visualization as there are fewer customization alternatives than other data visualization tools on our list. The price of Microsoft Power BI is quite pocket-friendly at $9.99 per user and can extend up to $15.99 per user, depending on the package. 

Conclusion

With the growing reliance on data volume available in the market, organizations have started realizing the power of data analytics, which can source real-time data internally and externally as a predictive and prescriptive source. However, to improve data analysis and visualization, engineers are required to select the right tool that aligns with their business goals and needs. Opting for the right tool will help in curating the vast amount of information without human error, eventually aiding in streamlining businesses.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post The Top Five Best Data Visualization Tools in 2024 first appeared on AI-Tech Park.

Joel, how have the unique challenges faced by small and medium-sized enterprises influenced their adoption of AI in identity management and security practices?

So we commission a biannual small to medium-sized enterprise (SME) IT Trends Report that looks specifically at the state of SME IT. This most recent version shows how quickly AI has impacted identity management and highlights that SMEs are kind of ambivalent as they look at AI. IT admins are excited and aggressively preparing for it—but they also have significant concerns about AI’s impact. For example, nearly 80% say that AI will be a net positive for their organization, 20% believe their organizations are moving too slowly concerning AI initiatives, and 62% already have AI policies in place, which is pretty remarkable considering all that IT teams at SMEs have to manage. But SMEs are also pretty wary about AI in other areas. Nearly six in ten (62%) agree that AI is outpacing their organization’s ability to protect against threats and nearly half (45%) agree they’re worried about AI’s impact on their job. I think this ambivalence reflects the challenges of SMEs evaluating and adopting AI initiatives – with smaller teams and smaller budgets, SMEs don’t have the budget, training, and staff their enterprise counterparts have. But I think it’s not unique to SMEs. Until AI matures a little bit, I think that AI can feel more like a distraction.

Considering your background in identity, what critical considerations should SMEs prioritize to protect identity in an era dominated by AI advancements?

I think caution is probably the key consideration. A couple of suggestions for getting started:

Data security and privacy should be the foundation of any initiative. Put in place robust data protection measures to safeguard against breaches like encryption, secure access controls, and regular security audits. Also, make sure you’re adhering to existing data protection regulations like GDPR and keep abreast of impending regulations in case new controls need to be implemented to avoid penalties and legal issues.

When integrating AI solutions, make sure they’re from reputable sources and are secure by design. Conduct thorough risk assessments and evaluate their data handling practices and security measures. And for firms working more actively with AI, research and use legal and technical measures to protect your innovations, like patents or trademarks.

With AI, it’s even more important to use advanced identity and authentication management (IAM) solutions so that only authorized individuals have access to sensitive data. Multi-factor authentication (MFA), biometric verification, and role-based access controls can significantly reduce that risk. Continuous monitoring systems can help identify and thwart AI-related risks in real time, and having an incident response plan in place can help mitigate any security breaches. 

Lastly, but perhaps most importantly, make sure that the AI technologies are used ethically, respecting privacy rights and avoiding bias. Developing an ethical AI framework can guide your decision-making process. Train employees on the importance of data privacy, recognizing phishing attacks, and secure handling of information. And be prepared to regularly update (and communicate!) security practices given the evolving nature of AI threats.

AI introduces both promises and risks for identity management and overall security. How do you see organizations effectively navigating this balance in the age of AI, particularly in the context of small to medium-sized enterprises?

First off, integrating AI has to involve more than just buzzwords – and I’d say that we still need to wait until AI accuracy is better before SMEs undertake too many AI initiatives. But at the core, teams should take a step back and ask, “Where can AI make a difference in our operations?” Maybe it’s enhancing customer service, automating compliance processes, or beefing up security. Before going all in, it’s wise to test the waters with pilot projects to get a real feel of any potential downstream impacts without overcommitting resources.

Building a security-first culture—this is huge. It’s not just the IT team’s job to keep things secure; it’s everybody’s business. From the C-suite to the newest hire, SMEs should seek to create an environment where everyone is aware of the importance of security, understands the potential threats, and knows how to handle them. And yes, this includes understanding the role of AI in security, because AI can be both a shield and a sword.

AI for security is promising as it’s on another level when it comes to spotting threats, analyzing behavior, and monitoring systems in real time. It can catch things humans might miss, but again, it’s VITAL to ensure the AI tools themselves are built and used ethically. AI for compliance also shows a lot of promise. It can help SMEs stay on top of regulations like GDPR or CCPA to avoid fines but also to build trust and reputation. 

Because there are a lot of known unknowns around AI, industry groups can be a good source for information sharing and collaboration. There’s wisdom and a strength in numbers and a real benefit in shared knowledge. It’s about being strategic, inclusive, ethical, and always on your toes. It’s a journey, but with the right approach, the rewards can far outweigh the risks.

Given the challenges in identity management across devices, networks, and applications, what practical advice can you offer for organizations looking to leverage AI’s strengths while addressing its limitations, especially in the context of password systems and biometric technologies?

It’s a surprise to exactly no one that passwords are often the weakest security link. We’ve talked about ridding ourselves of passwords for decades, yet they live on. In fact, our recent report just found that 83% of organizations use passwords for at least some of their IT resources. So I think admins in SMEs know well that despite industry hype around full passwordless authentication, the best we can do for now is to have a system to manage them as securely as possible. In this area, AI offers a lot. Adaptive authentication—powered by AI—can significantly improve an org’s security posture. AI can analyze things like login behavior patterns, geo-location data, and even the type of device being used. So, if there’s a login attempt that deviates from the norm, AI can flag it and trigger additional verification steps or step-up authentication. Adding dynamic layers of security that adapt based on context is far more robust than static passwords.

Biometric technologies offer a unique, nearly unforgeable means of identification, whether through fingerprints, facial recognition, or even voice patterns. Integrating AI with biometrics makes them much more precise because AI algorithms can process complex biometric data quickly, improve the accuracy of identity verification processes, and reduce the chances of both false rejections and false acceptances. Behavioral biometrics can analyze typing patterns, mouse or keypad movements, and navigation patterns within an app for better security. AI systems can be trained to detect pattern deviations and flag potential security threats in real time. The technical challenge here is to balance sensitivity and specificity—minimizing false alarms while ensuring genuine threats are promptly identified.

A best practice with biometrics is to employ end-to-end encryption for biometric data, both at rest and in transit. Implement privacy-preserving techniques like template protection methods, which convert biometric data into a secure format that protects against data breaches and ensures that the original biometric data cannot be reconstructed.

AI and biometric technologies are constantly evolving, so it’s necessary to keep your systems updated with the latest patches and software updates. 

How has the concept of “identity” evolved in today’s IT environment with the influence of AI, and what aspects of identity management have remained unchanged?

Traditionally, identity in the workplace was very much tied to physical locations and specific devices. You had workstations, and identity was about logging into a central network from these fixed points. It was a simpler time when the perimeter of security was the office itself. You knew exactly where data lived, who had access, and how that access was granted and monitored.

Now it’s a whole different ballgame. This is actually at the core of what JumpCloud does. Our open directory platform was created to securely connect users to whatever resources they need, no matter where they are. In 2024, identity is significantly more fluid and device-centered. Post-pandemic, and with the rise of mobile technology, cloud computing, and now the integration of AI, identities are no longer tethered to a single location or device. SMEs need for employees to be able to access corporate resources from anywhere, at any time, using a combination of different devices and operating systems—Windows, macOS, Linux, iOS, Android. This shift necessitates a move from a traditional, perimeter-based security model to what’s often referred to as a zero-trust model, where every access transaction needs to have its own perimeter drawn around it. 

In this new landscape, AI can vastly improve identity management in terms of data capture and analysis for contextual approaches to identity verification. As I mentioned, AI can consider the time of access, the location, the device, and even the behavior of the user to make real-time decisions about the legitimacy of an access request. This level of granularity and adaptiveness in managing access wasn’t possible in the past.

However, some parts of identity management have stayed the same. The core principles of authentication, authorization, and accountability still apply. We’re still asking the fundamental questions: “Are you who you say you are?” (authentication), “What are you allowed to do?” (authorization), and “Can we account for your actions?” (accountability). What has changed is how we answer these questions. We’re in the process of moving from static passwords and fixed access controls to more dynamic, context-aware systems enabled by AI.

In terms of identity processes and applications, what is the current role of AI for organizations, and how do you anticipate this evolving over the next 12 months?

We’re still a long away from the Skynet-type AI future that we’ve all associated with AI since the Terminator. For SMEs, AI accelerates a shift away from traditional IT management to an approach that’s more predictive and data-centric. At the core of this shift is AI’s ability to sift through vast, disparate data sets, identifying patterns, predicting trends, and, from an identity management standpoint, its power is in preempting security breaches and fraudulent activities. It’s tricky though, because you have to balance promise and risk, like legitimate concerns about data governance and the protection of personally identifiable information (PII). Tapping AI’s capabilities needs to ensure that we’re not overstepping ethical boundaries or compromising on data privacy. Go slow, and be intentional.

Robust data management frameworks that comply with evolving regulatory standards can protect the integrity and privacy of sensitive information. But keep in mind that no matter the benefit of AI automating processes, there’s a critical need for human oversight. The reality is that AI, at least in its current form, is best utilized to augment human decision-making, not replace it. As AI systems grow more sophisticated, organizations will require workers with  specialized skills and competencies in areas like machine learning, data science, and AI ethics.

Over the next 12 months, I anticipate we’ll see organizations doubling down on these efforts to balance automation with ethical consideration and human judgment. SMEs will likely focus on designing and implementing workflows that blend AI-driven efficiencies with human insight but they’ll have to be realistic based on available budget, hours, and talent. And I think we’ll see an increase in the push towards upskilling existing personnel and recruiting specialized talent. 

For IT teams, I think AI will get them closer to eliminating tool sprawl and help centralize identity management, which is something we consistently hear that they want. 

When developing AI initiatives, what critical ethical considerations should organizations be aware of, and how do you envision governing these considerations in the near future?

As AI systems process vast amounts of data, organizations must ensure these operations align with stringent privacy standards and don’t compromise data integrity. Organizations should foster a culture of AI literacy to help teams set realistic and measurable goals, and ensure everyone in the organization understands both the potential and the limitations of AI technologies.

Organizations will need to develop more integrated and comprehensive governance policies around AI ethics that address:

How will AI impact our data governance and privacy policies? 

What are the societal impacts of our AI deployments? 

What components should an effective AI policy include, and who should be responsible for managing oversight to ensure ethical and secure AI practices?

Though AI is evolving rapidly, there are solid efforts from regulatory bodies to establish frameworks, working toward regulations for the entire industry. The White House’s National AI Research and Development Strategic Plan is one such example, and businesses can glean quite a bit from that. Internally, I’d say it’s a shared responsibility. CIOs and CTOs can manage the organization’s policy and ethical standards, Data Protection Officers (DPOs) can oversee compliance with privacy laws, and ethics committees or councils can offer multidisciplinary oversight. I think we’ll also see a move toward involving more external auditors who bring transparency and objectivity.

In the scenario of data collection and processing, how should companies approach these aspects in the context of AI, and what safeguards do you recommend to ensure privacy and security?

The Open Worldwide Application Security Project (OWASP) has a pretty exhaustive list and guidelines. For a guiding principle, I’d say be smart and be cautious. Only gather data you really need, tell people what you’re collecting, why you’re collecting it, and make sure they’re okay with it. 

Keeping data safe is non-negotiable. Security audits are important to catch any issues early. If something does go wrong, have a plan ready to fix things fast. It’s about being prepared, transparent, and responsible. By sticking to these principles, companies can navigate the complex world of AI with confidence.

Joel Rennich

VP of Product Management at JumpCloud 

Joel Rennich is the VP of Product Strategy at JumpCloud residing in the greater Minneapolis, MN area. He focuses primarily on the intersection of identity, users and the devices that they use. While Joel has spent most of his professional career focused on Apple products, at JumpCloud he leads a team focused on device identity across all vendors. Prior to JumpCloud Joel was a director at Jamf helping to make Jamf Connect and other authentication products. In 2018 Jamf acquired Joel’s startup, Orchard & Grove, which is where Joel developed the widely-used open source software NoMAD. Installed on over one million Macs across the globe, NoMAD allows macOS users to get all the benefits of Active Directory without having to be bound to them. Joel also developed other open source software at Orchard & Grove such as DEPNotify and NoMAD Login. Over the years Joel has been a frequent speaker at a number of conferences including WWDC, MacSysAdmin, MacADUK, Penn State MacAdmins Conference, Objective by the Sea, FIDO Authenticate and others in addition to user groups everywhere. Joel spent over a decade working at Apple in Enterprise Sales and started the website afp548.com which was the mainstay of Apple system administrator education during the early years of macOS X.

The post AITech Interview with Joel Rennich, VP of Product Management at JumpCloud first appeared on AI-Tech Park.

Suppose you’ve been working on landing a high-value B2B client for months, writing a proposal that you believe is tailored to their needs. It explains your solution based on the technological features, comes with compelling references, and responds to their challenges. Yet, when the client responds with a simple “thanks, we’ll be in touch,” you’re left wondering: Was I heard? Was the intended message or the value provided by the product clear?

Here the shortcomings of conventional approaches to Natural Language Processing (NLP) in B2B communication manifest themselves…Despite these strengths, NLP tools are not very effective in understanding the nuances of B2B business and language and are rather limited in understanding the essence and intention behind the text. Common technical words in the document, rhetoric differences, and constant dynamics of the field that specialized terms reflect are beyond the capabilities of traditional NLP tools.

This is where Quantum Natural Language Processing (QNLP) takes the spotlight. It combines quantum mechanics with its ability to process language, making it more refined than previous AI systems. 

It’s like having the ability to comprehend not only the direct meaning of the text but also the tone, the humor references, and the business-related slang. 

QNLP is particularly rich for B2B professionals. This simply means that Through QNLP, companies and businesses can gain a deeper understanding of what the customer needs and what competitors are thinking, which in turn can re-invent the analysis of contracts to create specific marketing strategies.

 1. Demystifying QNLP for B2B professionals

B2B communication is all the more complex. Specificities in the contracts’ text, specific terminals, and constant changes in the industry lexicon represent the primary difficulty for traditional NLP. Many of these tools are based on simple keyword matches and statistical comparisons, which are capable of failing to account for the context and intention behind B2B communication.

This is where the progress made in artificial intelligence can be seen as a ray of hope. Emerging techniques like Quantum Natural Language Processing (QNLP) may bring significant shifts in the analysis of B2B communication. Now let’s get deeper into the features of QNLP and see how it can possibly revolutionize the B2B market.

1.1 Unveiling the Quantum Advantage

QNLP uses quantum concepts, which makes it more enhanced than other traditional means of language processing. Here’s a simplified explanation:

• Superposition: Think of a coin that is being rotated in the air with one side facing up; it has heads and tails at the same time until it falls. In the same way, QNLP can represent a word in different states at once, meaning that it is capable of capturing all the possible meanings of a certain word in a certain context.
  
• Entanglement: Imagine two coins linked in such a way that when one flips heads, the other is guaranteed to be tails. By applying entanglement, QNLP can grasp interactions as well as dependencies between words, taking into account not only isolated terms but also their interconnection and impact on the content of B2B communication.

By applying these nine concepts, QNLP is capable of progressing from keyword-based matching to understanding the B2B language landscape.

1.2 DisCoCat: The Framework for QNLP

The DisCoCat model is a mathematical framework for Distributed Correspondence Categorical Quantum Mechanics (DisCoCat) in language. It effectively enables QNLP to overlay the subtleties of B2B communication—be it contractual wording throughout specification documentation—in a format that is comprehensible and processable for quantum computing systems.

This creates opportunities for various innovative concepts in B2B communication. 

Imagine an AI that is not only capable of reading through the legal jargon of a contract but is also able to differentiate the connections between different clauses. It will also know whether there are gray areas in the document, and understand the overarching goal of the contract. Incorporated under DisCoCat, there is an enormous possibility that QNLP will transform how different businesses communicate, leading to a new paradigm shift of efficiency, accuracy, and understanding within the B2B environment. 

2. Potential Applications of QNLP in B2B

Most of the NLP tools lack the ability to unravel the nuanced flow of B2B communication. QNLP stands out as a revolutionizing tool for B2B professionals, transforming the strategies at their disposal. Let’s explore how QNLP unlocks valuable applications:

2.1 Enhanced Customer Insights: 

QNLP not only sees words but also sentiment, intent, and even purchasing behavior. This enables a B2B firm to know their customers inside and out, enabling them to predict the needs of the buyers and design better strategies for effective customer relations. 

2.2 Advanced Document Processing: 

The strength of QNLP lies in the fact that it can perform the extraction of relevant information with a higher degree of sensitivity due to the application of quantum mechanics. This eliminates manual processing bottlenecks, reduces mistakes, and improves important organizational activities. 

2.3 Personalized B2B Marketing: 

Through QNLP, B2B marketers can create content and campaigns that are tailored to niches and clients. By being able to better understand the customers and the market that the business operates in, QNLP allows companies to deliver messages that are not only relevant but can strike a chord with the audience, paving the way for better lead generation. 

2.4 Improved Chatbot Interactions: 

Chatbots are evolving the way B2B customer interactions occur. However, the usefulness of these tools is limited by their capability to deal with intricate questions. QNLP enhances chatbots to deal with customers’ interactions with more context awareness. Essentially, by analyzing these hard-to-detect prompts underlying the customers’ questions, QNLP-based chatbots are capable of delivering more adequate and beneficial answers that can enhance customer service. 

QNLP is a game-changer for the B2B channel of communication. By obtaining deeper insights into customer data, documents, and interactions, QNLP creates added benefits to B2B businesses in their strategic decision-making and organizational improvements with enhanced performance. 

3. The Road Ahead: QNLP and the Future of B2B Communication

It is worth stating that Quantum Natural Language Processing (QNLP) may exert a transformative influence on B2B communication. QNLP is yet in its infancy, and its capacity to understand the subtleties of complicated B2B jargon does not cease to amuse. Think about early warning systems that are able to log and process not only the quantity of information but also the qualitative psycho-emotional impact and purpose of B2B communication. 

Nonetheless, the use of QNLP to its full potential in a B2B environment depends on a collaborative attitude. It will be the work of quantum computing experts, NLP researchers, and business-to-business industry gurus who will do extensive research and development on this revolutionary technology for its continuous evolution.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post Quantum Natural Language Processing (QNLP): Enhancing B2B Communication first appeared on AI-Tech Park.

Introduction

The business landscape has been transformed over the past few years with the help of numerous technologies, and one such marvel is augmented intelligence, which has emerged as a potent ally for human users to enhance our business capabilities. This technology represents a synergy between human expertise and machine learning (ML), redefining how human intelligence approaches problem-solving, decision-making, and innovation. However, amidst all the insights, it is essential to understand that augmented intelligence is not a solution that can be operated independently. Still, it requires human oversight and intervention to help carefully orchestrate ethical considerations to ensure their alignment with human values and ideals.

In today’s AI Tech Park article, we will explore the boundless potential of augmented intelligence in reshaping the future of business.

A Symbiotic Relationship with Organizations and Augmented Intelligence

Augmented intelligence focuses on enhancing human capabilities by combining creativity and design-making skills with artificial intelligence’s (AI) ability to process large sets of data in a few seconds. For instance, in the healthcare sector, AI filters through millions of medical records to assist doctors in diagnosing and treating patients more effectively, therefore not replacing doctors’ expertise but augmenting it. Further, AI automates repetitive tasks, allowing human users to tackle more complex and creative work, especially with chatbots as they handle routine inquiries in customer service, allowing human agents to resolve more minute issues.

Augment intelligence uses personalized experience at a scale that informs users about current market trends, enhancing customer satisfaction, further helping to stimulate human creativity, and exploring new patterns and ideas. Numerous tools, such as OpenAI’s GPT-4 and Google Gemini, can create high-quality written content, which will assist writers and marketers in inefficiently generating social media posts and creative writing pieces. In terms of designing, genAI tools such as DALL-E and MidJourney work as guides that enable designers to generate unique images and artwork based on a few textual descriptions.

Real-World Business Scope of Augmented Intelligence

Till now, we have understood that augmented intelligence is the next thing that companies should implement in their businesses to kickstart a creative yet partially autonomous journey. Therefore, we bring you some key areas where augmented intelligence can have a significant impact:

1. Retail and Manufacturer Industry

The retail industry has witnessed a change in customer tastes post-COVID-19 pandemic, disrupting their logistical and supply chain structures. Therefore, retailers utilize augmented intelligence to analyze customer preference, purchase history, and browsing human behavior to deliver personalized product recommendations that not only enhance their shopping experience but also drive more sales and foster customer loyalty. On the other hand, the manufacturing industry has faced challenges such as supply chain disruptions, a massive drop in worker supply, and raw material shortages due to the pandemic and recession. Therefore, to curb these issues, B2B manufacturers rely on augmented intelligence that aids in data collection from sensors and IoT devices, which eventually helps them understand the production capacities of the production lines, the shipping times, warehousing space availability, and scheduling time for the workers.

2. Healthcare Industry

With the rise in patient personalization and medical and drug experiments, healthcare providers are leveraging augmented intelligence to analyze the largest amount of medical data and predict and diagnose diseases more accurately and efficiently. With the help of augmented analytics, hospitals and medical institutes can optimize their business operations by researching key metrics such as the duration of stay and the bed occupancy rate.

Bottom Line

The human-AI collaboration offers potential by leveraging the strengths of both human creativity and augmented intelligence to achieve shared objectives of better business operations. However, the implementation of this technology doesn’t imply the replacement of human intelligence, but this collaborative initiative will enhance decision-making, boost efficiency, and transform business interaction to enhance organization scalability and personalization.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post Enhancing Human Potential with Augmented Intelligence first appeared on AI-Tech Park.

Bernard, kindly brief us about Generative AI and its impact on various industries such as retail, healthcare, finance, education, manufacturing, marketing, entertainment, sports, coding, and more?

Generative AI (GenAI) is revolutionizing multiple sectors by enabling the creation of new, original content and insights. In retail, it’s personalizing shopping experiences; in healthcare, it’s accelerating drug discovery and patient care customization. Finance is seeing more accurate predictive models, while education benefits from tailored learning materials. Manufacturing, marketing, entertainment, sports, and coding are all experiencing unprecedented innovation and efficiency improvements, showcasing GenAI’s versatility and transformative potential.

Your latest book, “Generative AI in Practice,” is set to release soon. Could you share some key insights from the book, including how readers can implement GenAI, its differences from traditional AI, and the generative AI tools highlighted in the appendix?

In “Generative AI in Practice,” I explore how GenAI differs fundamentally from traditional AI by its ability to generate novel content and solutions. The book offers practical guidance on implementing GenAI, highlighting various tools and platforms in the appendix that can kickstart innovation in any organization. It’s designed to demystify GenAI and make it accessible to a broader audience.

With your extensive experience advising organizations like Amazon, Google, Microsoft, and others, what role do you see GenAI playing in transforming business strategies and performance?

It’s clear that Generative AI (GenAI) is poised to become a pivotal element in reshaping business strategies and boosting performance across industries. By leveraging GenAI, companies can gain a significant competitive advantage through the acceleration of innovation, the automation of complex and creative tasks, and the generation of actionable insights. This transformative technology enables businesses to refine their decision-making processes and enhance customer engagement in ways previously unimaginable. As we move forward, the integration of GenAI into core business operations will not only optimize efficiency but also open up new avenues for growth and value creation, marking a new era in the corporate landscape.

Why is Generative AI considered the most powerful technology humans have ever had access to, and what makes it stand out compared to other advancements in the tech industry?

Generative AI not only stands out as perhaps the most potent technology available today due to its capacity for creativity and innovation, surpassing prior tech advancements by enabling machines to understand, innovate, and create alongside humans, but it also offers a pathway to artificial general intelligence (AGI). This potential to achieve AGI, where machines could perform any intellectual task that a human can, marks a significant leap forward. It represents not just an evolution in specific capabilities, but a foundational shift towards creating systems that can learn, adapt, and potentially think with the breadth and depth of human intelligence. This aspect of generative AI not only differentiates it from other technological advancements but also underscores its transformative potential for the future of humanity.

GenAI brings forth unique risks and challenges. Can you discuss how businesses and individuals can navigate these challenges, especially in areas such as misinformation, disinformation, and deepfakes, particularly in an election year?

The unique risks and challenges presented by Generative AI, particularly in the realm of misinformation, disinformation, and the creation of deepfakes, demand a proactive and informed approach, especially during critical times such as election years. Businesses and individuals can navigate these challenges by adopting a commitment to ethical AI use, which includes the development and implementation of policies that emphasize accuracy and integrity. Additionally, investing in and utilizing advanced detection tools that can identify AI-generated misinformation or deepfakes is crucial. Equally important is the cultivation of GenAI literacy, ensuring that users can critically assess the information they encounter and understand its origins. This multi-pronged strategy is essential for safeguarding the informational ecosystem and maintaining public trust in digital content.

The impact of GenAI on the job market is a critical topic. What types of work do you anticipate being replaced or significantly altered by this groundbreaking technology, and how can individuals prepare for these changes?

The advent of Generative AI is set to significantly reshape the job market, introducing efficiencies that automate routine tasks, which could lead to the displacement of jobs in areas such as data entry, content creation, and customer service. Despite these disruptions, GenAI also promises the emergence of new job categories focused on AI supervision, ethical governance, and the creative industries, reflecting the technology’s dual impact on the workforce. To navigate this evolving landscape, individuals must prioritize lifelong learning and skill development, focusing on areas that AI is unlikely to replicate easily, such as creative problem-solving, emotional intelligence, and ethical decision-making. By adapting to the changes brought about by GenAI, workers can prepare for and thrive in the new job market dynamics it creates.

In your forthcoming book, you touch on how GenAI interacts with other transformative technologies. How do you foresee GenAI collaborating with gene editing, immersive internet, conventional AI, blockchain, quantum computing, etc., to create a world of hyper-innovation?

I explore the transformative potential of Generative AI (GenAI) as it intersects with groundbreaking technologies such as gene editing, the immersive internet, conventional AI, blockchain, and quantum computing, heralding a future of hyper-innovation. GenAI’s capability to produce novel content and solutions enhances gene editing for personalized medicine, enriches the immersive internet with dynamic virtual experiences, and augments conventional AI’s problem-solving abilities. In combination with blockchain, it promises more secure and efficient transaction systems, while its integration with quantum computing could revolutionize our approach to complex challenges, from material science to cryptography. This synergy across technologies suggests a paradigm shift towards a future where the acceleration of breakthroughs across fields from medicine to environmental science could vastly expand the horizons of human capability and knowledge.

Ethical concerns surrounding GenAI, including misinformation and deepfakes, are important considerations. What measures do you believe should be taken to address these concerns and ensure responsible use of Generative AI?

To effectively address the ethical concerns surrounding Generative AI, a multi-faceted approach is essential. This includes establishing transparency in AI development and deployment processes, adhering to rigorous ethical standards that are continuously updated to reflect emerging challenges, and actively engaging the public and stakeholders in discussions about AI’s societal impacts. Furthermore, the development of robust guidelines and regulatory frameworks for responsible AI use is critical, not only to mitigate risks like misinformation and deepfakes but also to foster trust and understanding among users. Such measures should aim to balance innovation with ethical considerations, ensuring GenAI serves the public good while minimizing potential harms.

Everyday activities are expected to be impacted by GenAI. Could you provide examples of how GenAI will influence tasks like searching for information, cooking, and travel in the near future?

Generative AI is poised to revolutionize everyday activities by enhancing efficiency and personalization. In the realm of information search, GenAI can provide more accurate and context-aware results, effectively understanding and anticipating user needs. For cooking, it could offer recipe customization based on dietary preferences, available ingredients, or desired cuisine, making meal planning simpler and more enjoyable. When it comes to travel, GenAI can tailor recommendations for destinations, accommodations, and activities to individual tastes and requirements, simplifying the planning process and enhancing the travel experience. These examples illustrate just a few ways GenAI will make everyday tasks more intuitive, enjoyable, and aligned with personal preferences.

In tracing the evolutionary blueprint of GenAI, from the 1950s to today, what key milestones and developments have played a significant role in shaping its current capabilities and applications?

The journey of Generative AI from its nascent stages in the 1950s to its current state has been marked by several pivotal milestones. The invention of neural networks laid the foundational architecture for AI to process information in a manner akin to the human brain. Subsequent advancements in machine learning algorithms have dramatically improved AI’s ability to learn from data, leading to more sophisticated and capable AI systems. The launch of platforms capable of generating human-like text and understanding natural language has significantly broadened GenAI’s applications, enabling it to write articles, compose music, develop code, and more. These key developments have not only advanced the capabilities of GenAI but also expanded its potential applications, setting the stage for its continued evolution and growing impact on society.

Bernard Marr

CEO and Founder of Bernard Marr & Co.

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity.

He is a multi-award-winning and internationally best-selling author of over 20 books, writes a regular column for Forbes and advises and works with many of the world’s best-known organisations.

He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world.

The post AITech Interview with Bernard Marr, CEO and Founder of Bernard Marr & Co. first appeared on AI-Tech Park.

Table of contents:
1. Real-time Analytics and Streaming Data in Depth
1.1 What is Real-time Analytics?
1.2 What is Streaming Data?
2. Key Components and Technologies
3. Powering Business Growth with Streaming Data
3.1 Financial Services
3.2 Healthcare
3.3 Retail
3.4 Manufacturing
4. The Future of Real-time Analytics with Streaming Data

As the business world revolves around globalization and faster results, top executives, data analysts, and even marketing managers look forward to real-time analytics. It enables them to harness the power of streaming data in their business and gain a vast amount of valuable information that can inspire the growth of the business.

A manufacturing giant takes global production to the next level by leveraging real-time analytics to predict equipment breakdowns before they happen, boosting productivity across all departments. This is the power of real-time analytics and this is where the real potential for any business is hidden: the potential to turn into the industry leader.

Real-time analytics enables you to possess the flexibility and vision to trump your rivals while building toward stable revenue decades ahead.

Q. What is Real-time analytics and streaming data?

Real-time analytics could be defined as data analysis that takes place with maximum efficiency, and within a short period, which will allow businesses to constantly adapt to events and make the correct decisions based on that data.

Real-time analytics uses streaming data as its primary source for feeding data into the analysis process. It is a stream of data that emanates from numerous sources, such as sensors, social sites, customers, and monetary transactions, for example. While the traditional batch method has a rigid approach that analyzes data at fixed intervals, streaming data analysis occurs on the spot from time to time.

This blog is your roadmap to making sense of real-time analytics, streaming data, and what’s next. Here, we will discuss and give evidence of the benefits that users will realize from this technology, review the enabling technologies required for real-time analytics, and explain, in detail, the different elements that are required to achieve reliable big data real-time analytics within organizations.

1. Real-time Analytics and Streaming Data in Depth

The ability to digest information as it is received and not wait longer is very useful in today’s information society. This is where real-time analytics comes in.

It elaborates on the results being acquired instantly, which allows for a flexible and immediate response to the needs of the business.

1.1. What is Real-time Analytics?

Real-time analytics is a way of getting insights from data as soon as it arrives. Real-time, in the context of big data, refers to analytics that are provided once the data has been processed, but without the delays of traditional batch processing. 

Real-time data visibility helps businesses respond to events in real-time, make timely decisions, and formulate strategies, especially when they notice deviations from the normal trend.

1. 2. What is Streaming Data?

In real-time analytics, the lifeblood is derived from streaming data, which means data is continually fed from various sources. Think of the feeder being on constantly and pumping data into your analytics centre. Some B2B examples include:

• Social media feeds – analyzing real-time sentiment about your brand and ads,
• IoT sensor data for factory machinery, supply chain, and building energy,
• Financial transactions to prevent and report fraud and embezzlement, more and less profits, 
• Customers’ website activity to monitor the behaviour and marketing strategy, and predict potential paying consumers.

2. Key Components and Technologies

Organizations need to be equipped with an analytics platform that delivers real-time data for efficient strategic decision-making all over the pyramid. By leveraging the use of data ingestion tools such as Kafka and Flume, you would be in a good position to transfer stream data without interfering with your current systems. Apache Spark or Flink and other appropriate iterative stream processing frameworks facilitate real-time analysis, which in turn helps to respond actively to the changes occurring in the market and customers’ behaviour.

For faster access to data, implement in-memory databases like Redis for a fast scan of the data, or the scalability aspects provided by Cassandra or MongoDB. Last of all, BI tools such as Grafana or Tableau facilitate concise and effective communication of insights to the parties concerned, as it helps correlate with the narrative.

In today’s faster and more complex B2B environment, real-time analytical capability is not a frill, but a necessity. If businesses incorporate these constituents and technologies into their solutions. They can fully harness the power of streaming data and make a tangible business impact.

3. Powering Business Growth with Streaming Data

The change to massive quantities of data is ongoing and real-time analytics has become the latest buzzword. By using streaming data, it becomes possible to garner a lot of information and help diverse business organizations make decisions faster and more accurately.

3. 1 Financial Services: 

Chief Risk Officers and Fraud Analysts: 

Real-time solutions allow fraud analysts or risk officers to respond in real-time to fraudulent activities protecting the financial health of an organization.

Investment Professionals and Traders: 

Unlock rapid business results with timely recommendations as the market moves. Breathtaking market insights and instant visualization of investments and trades make this technology uniquely efficient for professional investors and traders

3. 2 Healthcare: 

Physicians and Care Teams: 

Continual patient monitoring also eliminates the need to wait for the results in an emergency, allowing physicians or healthcare teams to adjust the course of treatment in the blink of an eye.

Healthcare Administrators and Public Health Officials: 

Using predictive capabilities, healthcare professionals can identify probable disease epidemics and, as a result, direct resources effectively, enabling preventive healthcare administration.

3.3 Retail: 

Marketing Directors and Customer Relationship Managers: 

CRMs and MDs can create effective and highly targeted customer interactions in real-time. Another aspect of customer-oriented strategies is to utilize available information to better address clients’ wants and needs to increase their interest and commitment.

Supply Chain Managers and Inventory Control Specialists: 

SCMs and Inventory control specialists can work with the suitable inventory with real-time analytics help. Eliminate the occurrence of stockouts, cut down on related expenses, and optimize all aspects of managing your stocks.

3.4 Manufacturing: 

Operations Managers and Maintenance Engineers: 

The adoption of condition-based monitoring and real-time analysis can be done by operation managers and maintenance engineers to plan out maintenance schedules. Detect potential faults in the equipment before they lead to stoppages, thus reducing downtimes while boosting productivity.

Supply Chain and Logistics Leaders: 

Logistics and supply chain leaders can do real-time supply chain monitoring. Manage delivery schedules to gain the most effective route plans, manage disruptions, and ensure that your product gets to your clients on time.

Real-time analytics and streaming data are not restricted to a certain field and are the master key that opens a business up for growth. With raw data feeding into systems in real-time as the fourth industrial revolution rapidly unfolds, organizations that adopt this disruptive innovation will stand to benefit from the evolving business environment.

4. The Future of Real-time Analytics with Streaming Data

The integration of real-time analytics with AI and machine learning will provide a level of flexibility in the future of businesses that are unimagined.With these powerful combinations, businesses will be able to prevent, recover, and gain insights into processes, customers, and markets in real time.

In addition, the growth of the edge computing model suggests that data processing will occur in more localized settings, which will further reduce latency. This is especially true for industries such as manufacturing, where monitoring of production lines will be done in real time and can help avoid a range of expensive losses.

Real-time analytics is still a relatively young field, but as more and more organizations realize its potential, it can be expected that more diverse fields of business and industry will start utilizing it. Closely related, from third-party logistics providers seeking to improve the efficiency of delivery routes to banking institutions, hoping to identify suspect transactions, the possibilities are endless. The current trends of implementation and scaling point towards a future rich in new technologies and Business Intelligence (BI) mechanisms. This highlights the ongoing development driven by the increasing demand for real-time data analysis. Real-time analytics with streaming data is not something that businesses should just pursue as the latest trend; it is the proactive force that will radically alter the nature of business in years to come. Thanks to this technology and its updates, companies can achieve a competitive advantage and a sustainable development trajectory.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post Real-time Analytics: Business Success with Streaming Data first appeared on AI-Tech Park.

Table of content
Introduction
1. Yellowfin
2. Sisense
3. QlikView
4. Kyligence
5. Tableau
Winding Up

Introduction

In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future. 

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise. 

In today’s exclusive AITech Park article, we will take a quick look at the top five augmented analytics tools that data scientist teams can depend on to democratize advanced-level analytics with augmented data ingestion, data preparation, analytics content, and DSML model development. 

1. Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance. 

2. Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed. 

3. QlikView

QlikView is well-known for its data visualization, analytics, and BI solution that aids IT organizations in making data-based strategic decisions with the help of sophisticated analytics and insights drawn from multiple data sources. The platform allows data scientists to develop, extend, and embed visual analytics in existing applications and portals while adhering to governance and security frameworks. However, some users have reported that the software may slow down when assembling large datasets. Additionally, the software sometimes lacks the desired feature and depends mostly on plugins from the older QlikView, which lacks compatibility with the updated Qlik Sense. The QlikView comes in three pricing plans: Standard Plan: $20/mo for 10 full users only, with up to 50GB/year data for analysis, Premium Plan: starts at $2,700/mo and 50GB/yr data for analysis and more advanced features and  Enterprise Plan: Custom pricing, starting at 500GB/yr data for analysis.

4. Kyligence

The fourth augmented analytics tool that data scientist teams use is Kyligence, as it stands out for its automated insights and NLP technology for businesses to generate deep insights within seconds. The technology also promises a centralized, low-code platform that emphasizes a metrics-driven approach to business decision-making and further identifies the ups and downs of the given metrics, along with discovering root causes and generating reports within seconds. However, the tools are considered to be quite complex and expensive when compared to other augmented analytics tools on the market. Kyligence comes in three pricing plans. Standard plan: $59/user/month, Premium plan: $49/user/month (minimum 5 users), and Enterprise+ plan: Flexible pricing and deployment options.

5. Tableau

Last but not least, we have the famous Tableau, an integrated BI and analytics solution that will help in acquiring, producing, and analyzing the company’s data and provide insightful information. Data scientists can use Tableau to collect information from a variety of sources, such as spreadsheets, SQL databases, Salesforce, and cloud applications. Talking about the interface, it is quite easy regardless of your technical skills, allowing you to explore and visualize data effortlessly, but professionals at an executive level might have issues adapting to this technology. However, the most concerning part of this software is its high pricing and lack of customization in terms of visualization options. In terms of pricing, Tableau comes with two exclusive plans: for an individual user, it is $75/month, and for two users, it is $150/month.

Winding Up

With the importance of data, data analytics, and augmented analytics tools, data scientists are paving the way for effortless and informed decision-making. The five tools listed above are designed to automate the complex data analysis process.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post The Top Five Best Augmented Analytics Tools of 2024! first appeared on AI-Tech Park.

Background:

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

Code optimization in AI is all about maximizing results while minimizing inefficiencies and operational costs. It’s a key factor in driving the success and sustainability of AI initiatives in the dynamic and competitive landscape of modern businesses.

Code Optimization:

What are some common challenges and issues businesses face with code optimization when implementing AI solutions?

Businesses implementing AI solutions often encounter several challenges with code optimization, mainly due to the dynamic and complex nature of AI systems compared to traditional software optimization. Achieving optimal AI performance requires a delicate balance between code, model, and data, making the process intricate and multifaceted. This complexity is compounded by the need for continuous adaptation of AI systems, as they require constant updating to stay relevant and effective in changing environments.

A significant challenge is the scarcity of skilled performance engineers, who are both rare and expensive. In cities like London, costs can reach up to £500k per year, making expertise a luxury for many smaller companies.

Furthermore, the optimization process is time- and effort-intensive, particularly in large codebases. It involves an iterative cycle of fine-tuning and analysis, demanding considerable time even for experienced engineers. Large codebases amplify this challenge, requiring significant manpower and extended time frames for new teams to contribute effectively.

These challenges highlight the necessity for better tools to make code optimization more accessible and manageable for a wider range of businesses.

Could you share some examples of the tangible benefits businesses can achieve through effective code optimization in AI applications?

AI applications are subject to change along three axes: model, code, and data. At TurinTech, our evoML platform enables users to generate and optimize efficient ML code. Meanwhile, our GenAI-powered code optimization platform, Artemis AI, can optimize more generic application code. Together, these two products help businesses significantly enhance cost-efficiency in AI applications.

At the model level, different frameworks or libraries can be used to improve model efficiency without sacrificing accuracy. However, transitioning an ML model to a different format is complex and typically requires manual conversion by developers who are experts in these frameworks.

At TurinTech AI, we provide advanced functionalities for converting existing ML models into more efficient frameworks or libraries, resulting in substantial cost savings when deploying AI pipelines.

One of our competitive advantages is our ability to optimize both the model code and the application code. Inefficient code execution, which consumes excess memory, energy, and time, can be a hidden cost in deploying AI systems. Code optimization, often overlooked, is crucial for creating high-quality, efficient codebases. Our automated code optimization features can identify and optimize the most resource-intensive lines of code, thereby reducing the costs of executing AI applications.

Our research at TurinTech has shown that code optimization can improve the execution time of specific ML codebases by up to around 20%. When this optimized code was tested in an Azure-based cloud environment, we observed cost savings of about 30% per hour for the virtual machine size used. This highlights the significant impact of optimizing both the model and code levels in AI applications.

Are there any best practices or strategies that you recommend for businesses to improve their code optimization processes in AI development?

Code optimization leads to more efficient, greener, and cost-effective AI. Without proper optimization, AI can become expensive and challenging to scale.

Before embarking on code optimization, it’s crucial to align the process with your business objectives. This alignment involves translating your main goals into tangible performance metrics, such as reduced inference time and lower carbon emissions.

Empowering AI developers with advanced tools can automate and streamline the code optimization process, transforming what can be a lengthy and complex task into a more manageable one. This enables developers to focus on more innovative tasks.

In AI development, staying updated with AI technologies and trends is crucial, particularly by adopting a modular tech stack. This approach not only ensures efficient code optimization but also prepares AI systems for future technological advancements.

Finally, adopting eco-friendly optimization practices is more than a cost-saving measure; it’s a commitment to sustainability. Efficient code not only reduces operational costs but also lessens the environmental impact. By focusing on greener AI, businesses can contribute to a more sustainable future while reaping the benefits of efficient code.

Generative AI and Its Impact:

Generative AI has been a hot topic in the industry. Could you explain what generative AI is and how it’s affecting businesses and technology development?

Generative AI, a branch of artificial intelligence, excels in creating new content, such as text, images, code, video, and music, by learning from existing datasets and recognizing patterns.

Its swift adoption is ushering in a transformative era for businesses and technology development. McKinsey’s research underscores the significant economic potential of Generative AI, estimating it could contribute up to $4.4 trillion annually to the global economy, primarily through productivity enhancements.

This impact is particularly pronounced in sectors like banking, technology, retail, and healthcare. The high-tech and banking sectors, in particular, stand to benefit significantly. Generative AI is poised to accelerate software development, revolutionizing these industries with increased efficiency and innovative capabilities. We have observed strong interest from these two sectors in leveraging our code optimization technology to develop high-performance applications, reduce costs, and cut carbon emissions.

Are there any notable applications of generative AI that you find particularly promising or revolutionary for businesses?

Generative AI presents significant opportunities for businesses across various domains, notably in marketing, sales, software engineering, and research and development. According to McKinsey, these areas account for approximately 75% of generative AI’s total annual value.

One of the standout areas of generative AI application is in data-driven decision-making, particularly through the use of Large Language Models (LLMs). LLMs excel in analyzing a wide array of data sources and streamlining regulatory tasks via advanced document analysis. Their ability to process and extract insights from unstructured text data is particularly valuable. In the financial sector, for instance, LLMs enable companies to tap into previously underutilized data sources like news reports, social media content, and publications, opening new avenues for data analysis and insight generation.

The impact of generative AI is also profoundly felt in software engineering, a critical field across all industries. The potential for productivity improvements here is especially notable in sectors like finance and high-tech. An interesting trend in 2023 is the growing adoption of AI coding tools by traditionally conservative buyers in software, such as major banks including Citibank, JPMorgan Chase, and Goldman Sachs. This shift indicates a broader acceptance and integration of AI tools in areas where they can bring about substantial efficiency and innovation.

How can businesses harness the potential of generative AI while addressing potential ethical concerns and biases?

The principles of ethical practice and safety should be at the heart of implementing and using generative AI. Our core ethos is the belief that AI must be secure, reliable, and efficient. This means ensuring that our products, including evoML and Artemis AI, which utilize generative AI, are carefully crafted, maintained, and tested to confirm that they perform as intended.

There is a pressing need for AI systems to be free of bias, including biases present in the real world. Therefore, businesses must ensure their generative AI algorithms are optimized not only for performance but also for fairness and impartiality. Code optimization plays a crucial role in identifying and mitigating biases that might be inherent in the training data and reduces the likelihood of these biases being perpetuated in the AI’s outputs.

More broadly, businesses should adopt AI governance processes that include the continuous assessment of development methods and data and provide rigorous bias mitigation frameworks. They should scrutinize development decisions and document them in detail to ensure rigor and clarity in the decision-making process. This approach enables accountability and answerability.

Finally, this approach should be complemented by transparency and explainability. At TurinTech, for example, we ensure our decisions are transparent company-wide and also provide our users with the source code of the models developed using our platform. This empowers users and everyone involved to confidently use generative AI tools.

The Need for Sustainable AI:

Sustainable AI is becoming increasingly important. What are the environmental and ethical implications of AI development, and why is sustainability crucial in this context?

More than 1.3 million UK businesses are expected to use AI by 2040, and AI itself has a high carbon footprint. A University of Massachusetts Amherst study estimates that training a single Natural Language Processing (NLP) model can generate close to 300,000 kg of carbon emissions.

According to an MIT Technology Review article, this amount is “nearly five times the lifetime emissions of the average American car (and that includes the manufacture of the car itself).” With more companies deploying AI at scale, and in the context of the ongoing energy crisis, the energy efficiency and environmental impact of AI are becoming more crucial than ever before.

Some companies are starting to optimize their existing AI and code repositories using AI-powered code optimization techniques to address energy use and carbon emission concerns before deploying a machine learning model. However, most regional government policies have yet to significantly address the profound environmental impact of AI. Governments around the world need to emphasize the need for sustainable AI practices before it causes further harm to our environment.

Can you share some insights into how businesses can achieve sustainable AI development without compromising on performance and innovation?

Sustainable AI development, where businesses maintain high performance and innovation while minimizing environmental impact, presents a multifaceted challenge. To achieve this balance, businesses can adopt several strategies.

Firstly, AI efficiency is key. By optimizing AI algorithms and code, businesses can reduce the computational power and energy required for AI operations. This not only cuts down on energy consumption and associated carbon emissions but also ensures that AI systems remain high-performing and cost-effective.

In terms of data management, employing strategies like data minimization and efficient data processing can help reduce the environmental impact. By using only the data necessary for specific AI tasks, companies can lower their storage and processing requirements.

Lastly, collaboration and knowledge sharing in the field of sustainable AI can spur innovation and performance. Businesses can find novel ways to develop AI sustainably without compromising on performance or innovation by working together, sharing best practices, and learning from each other.

What are some best practices or frameworks that you recommend for businesses aiming to integrate sustainable AI practices into their strategies?

Creating and adopting energy-efficient AI models is particularly necessary for data centers. While this is often overlooked by data centers, using code optimization means that traditional, energy-intensive software and data processing tasks will consume significantly less power.

I would then recommend using frameworks such as a carbon footprint assessment to monitor current output and implement plans for reducing these levels. Finally, overseeing the lifecycle management of AI systems is crucial, from collecting data and creating models to scaling AI throughout the business.

Final Thoughts:

In your opinion, what key takeaways should business leaders keep in mind when considering the optimization of AI code and the future of AI in their organizations?

When considering the optimization of AI code and its future role in their organizations, business leaders should focus on several key aspects. Firstly, efficient and optimized AI code leads to better performance and effectiveness in AI systems, enhancing overall business operations and decision-making.

Cost-effectiveness is another crucial factor, as optimized code can significantly reduce the need for computational resources. This lowers operational costs, which becomes increasingly important as AI models grow in complexity and data requirements. Moreover, future-proofing an organization’s AI capabilities is essential in the rapidly evolving AI landscape, with code optimization ensuring that AI systems remain efficient and up-to-date.

With increasing regulatory scrutiny on AI practices, optimized code can help ensure compliance with evolving regulations, especially in meeting ESG (Environmental, Social, and Governance) compliance goals. It is a strategic imperative for business leaders, encompassing performance, cost, ethical practices, scalability, sustainability, future-readiness, and regulatory compliance.

As we conclude this interview, could you provide a glimpse into what excites you the most about the intersection of code optimization, AI, and sustainability in business and technology?

Definitely. I’m excited about sustainable innovation, particularly leveraging AI to optimize AI and code. This approach can really accelerate innovation with minimal environmental impact, tackling complex challenges sustainably. Generative AI, especially, can be resource-intensive, leading to a higher carbon footprint. Through code optimization, businesses can make their AI systems more energy-efficient.

Secondly, there’s the aspect of cost-efficient AI. Improved code efficiency and AI processes can lead to significant cost savings, encouraging wider adoption across diverse industries. Furthermore, optimized code runs more efficiently, resulting in faster processing times and more accurate results.

Do you have any final recommendations or advice for businesses looking to leverage AI optimally while remaining ethically and environmentally conscious?

I would say the key aspect to embody is continuous learning and adaptation. It’s vital to stay informed about the latest developments in AI and sustainability. Additionally, fostering a culture of continuous learning and adaptation helps integrate new ethical and environmental standards as they evolve.

Leslie Kanthan

Chief  Executive Officer and Founder at TurinTech AI

Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform – EvoML – for building and scaling AI.

The post AI-Tech Interview with Leslie Kanthan, Chief Executive Officer and Founder at TurinTech AI first appeared on AI-Tech Park.

Table of contents
Introduction
1. Getting Started with Platform Engineering
2. The Top Three Platform Engineering Tools You Should Consider in 2024
2.1. Crossplane
2.2. Port
2.3. ArgoCD
Conclusion

Introduction

Platform engineering is considered a practice built up on DevOps guides that assist in improving each development team’s compliance, costs, security, and business processes, eventually helping to improve developer experiences and self-service within a secure, governed framework. 

Lately, there has been quite a buzz about the permanent implementation of platform engineering in the IT industry. According to a recent report by Gartner, it is estimated that more than 80% of engineering organizations will have a crew dedicated to platform engineering by 2026, where these teams will focus on building an internal developer platform. This also implies that regardless of the business domain, these platforms by nature will help in achieving high business scale and reduce the time it takes to deliver business value. 

In today’s exclusive AI TechPark article, we will help IT developers understand the need for platform engineering along with the top three trending tools they can use for an easy business operation. 

1. Getting Started with Platform Engineering

Platform engineering is not for every company; for instance, in fledgling startups, where every individual does a bit of everything, this guide doesn’t come in handy. On the other hand, for companies that have two or more app teams where duplicate efforts are observed, platform engineering is the best option to tackle that toil, allowing the developers to think outside the box.

The best way to start the platform engineering journey in your organization is to have a conversation with the team of engineers, allowing them to understand and survey bottlenecks and developer frustrations, further advising the use of platform engineering that embeds and pairs programming within application teams.

During the process of building an application, developers need to question the size of the requirements, patterns, and trends needed in the app, the bottlenecks, and many more. However, it doesn’t end here, as to further comprehend the application, they require multiple testing and opinion polls by their internal customers; developers are also required to document every minute detail and change on the platform to encourage self-service and independence in the long run. 

Therefore, whether it is infrastructure provisioning, code pipelines, monitoring, or container management, the self-service platform will be a guide to hiding these complexities and providing developers with the necessary tools and applications. 

2. The Top Three Platform Engineering Tools You Should Consider in 2024

In this section, we will introduce you to the top three tools that every platform engineer should try in 2024 to perform routine tasks without being time-consuming and with zero human errors. 

2.1. Crossplane

Navigating the intricate landscape of Kubernetes infrastructure, Crossplane is one of the best platform engineering tools that securely builds a control plane with its tailored and unique needs without writing tricky distributed systems code. Crossplane is a master orchestrator that extends beyond container management, as its reliability and security are inherent to Kubernetes. 

2.2. Port

Port emerges as an indispensable asset of platform engineering, offering DevOps teams a centralized platform for orchestrating applications and infrastructure with unparalleled precision and control. The platform has a unique blend of oversight and flexibility that allows IT managers to maintain standards and best practices to streamline the business process effectively and efficiently. 

2.3. ArgoCD

Argo CD, a Kubernetes-native marvel, has redefined the landscape of modern application deployment. It offers a meticulous orchestration of deployment processes, ensuring that the applications are not just deployed but thriving and in sync with the demands of the tech world. The platform empowers developers to take full command, seamlessly managing both the intricate web of infrastructure configurations and the pulsating lifeline of application updates, all within a single, unified system.

Conclusion

Platform engineering is considered the optimal suite of tools that aids in orchestrating a symphony of tools that align with developers’ unique operational needs and aspirations while also keeping cost, skillset compatibility, feature sets, and user interface design in consideration.

Explore AITechPark for the latest advancements in AI, IOT, Cybersecurity, AITech News, and insightful updates from industry experts!

The post Understanding the Top Platform Engineering Tools of 2024 first appeared on AI-Tech Park.

Introduction

Cloud computing has opened new doors for business applications and programs to utilize databases to store data every day worldwide. These databases are well-known for securing data and making it accessible only to channels where the chief data officer (CDO) permits. Previously, organizations depended on database-paid suites, which were expensive and limited in options; however, now IT organizations have open-source databases for all their data, as these are affordable and flexible. However, it is often difficult to find the right cloud database service provider that will not only store the data of your company but also transfer it to the database, while data professionals can access it anywhere with an internet connection.

In this review article by AITech Park, we will explore the top five open-source cloud databases that can be used by IT professionals to build robust applications.

1. SQLite

SQLite is recognized as one of the most lightweight embedded relational database management systems (RDBMS) that operate inside applications. To power this embedded database, SQLite has a fully functional application that works as a library that supports ACID transactions. The software has an embedded library that has an SQL database engine supporting ACID transactions, which further reads and writes data through tables, indices, triggers, and views that can be contained in a single file. With a recent update on SQLite, data professionals and developers can use this software in the form of mobile applications, web browsers, and IoT devices, allowing smaller digital footprints and less load on the software.

2. MariaDB

MariaDB is considered one of the clones of MySQL as it was built on the same code; however, over the years, it has developed to be user-friendly for executive-level data professionals. With newer updates, MariaDB operates on the Aria storage engine to conduct complex SQL queries, ultimately giving it a speed boost over MySQL. The most unique feature of this open-source database is that it allows pluggable storage engines, allowing data teams to go beyond normal transactional processing. For instance, teams can use ColumStore for high-volume data storage and distribution. The ColumnStore can also be used for columnar analytics and hybrid smart transactions (HTAP), which improve data replication and support many JSON functions.

3. Apache CouchDB

CouchDB by Apache is a database duplication tool that deters data loss in the event of network failure or any other pipeline failure. The software creates a dedicated database system that can operate efficiently on ordinary hardware, not just by deploying on one server node but also as a single analytical system across numerous nodes in a cluster, which can be mounted as needed by adding more servers. For a seamless operation, the database uses JSON documents to store data and JavaScript as its query language. Further, it also supports MVCC and the ACID properties in individual documents.

4. MySQL

MySQL is one of the most popular and oldest open-source databases, and it is known as its best database for web-based apps such as Trello and Gmail. The database software uses the Structured Query Language (SQL), which lets data professionals store data in tables, develop indexes on the data, and query the data. MySQL supports an expansive variety of techniques and has a very low probability of getting the data corrupted as it gears for transactional uses, further supporting analytics and machine learning (ML) applications.

5. PostgreSQL

PostgreSQL became popular among data professionals and developers around 1995 when it started working as a SQL language interpreter, and decades later it became a popular open-source cloud database. This database software offers full RDBMS features, such as ACID compliance, SQL querying, and clearance for procedural language queries to develop stored procedures and stimuli in databases. PostgreSQL also supports enterprise applications that demand complex transactions and high levels of concurrency, and occasionally for data warehousing. It also supports multi-version concurrency control (MVCC), so data can be read and edited by various users at the same time, and it also sustains other varieties of database objects.

Conclusion

To create any kind of app, developers and data professionals need a secured database where they can save files and confidential data required for numerous use cases. While we are well aware that closed databases are expensive and use licensed codes, the above open-source database software provides data engineers with the flexibility to build their own DBMS without breaking the bank.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

The post Unlocking the Top Five Open-Source Database Management Software first appeared on AI-Tech Park.