AI-Tech Interview with Leslie Kanthan, CEO and Founder at TurinTech AI

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.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-leslie-kanthan/ 

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Understanding AI Bias and Why Human Intelligence Cannot Be Replaced

AI bias has the potential to cause significant damage to cybersecurity, especially when it is not controlled effectively. It is important to incorporate human intelligence alongside digital technologies to protect digital infrastructures from causing severe issues.

AI technology has significantly evolved over the past few years, showing a relatively nuanced nature within cybersecurity. By tapping into vast amounts of information, artificial intelligence can quickly retrieve details and make decisions based on the data it was trained to use. The data can be received and used within a matter of minutes, which is something that human intelligence might not be able to do.

With that said, the vast databases of AI technologies can also lead the systems to make ethically incorrect or biased decisions. For this reason, human intelligence is essential in controlling potential ethical errors of AI and preventing the systems from going rogue. This article will discuss why AI technology cannot fully replace humans and why artificial intelligence and human intelligence should be used side-by-side in security systems.

Inherent Limitations of AI

AI technology has significantly improved throughout the years, especially regarding facial recognition and other security measures. That said, while its recognition abilities have become superior, it is still lacking when it comes to mimicking human judgment.

Human intelligence is influenced by factors like intuition, experience, context, and values. This allows humans to make decisions while considering different perspectives, which may or may not be present in a data pool. As AI systems are still far from being perfectly trained with all the information in the world, they can present errors in judgment that could have otherwise not happened with human intelligence.

AI data pools also draw information from “majorities,” registering through information that was published decades ago. Unless effectively trained and updated, it may be influenced by information that is now irrelevant. For instance, AI could unfairly target specific groups subjected to stereotypes in the past, and the lack of moral compass could create injustice in the results.

One significant problem of using AI as the sole system for data gathering is that it can have substantial limitations in fact-checking. Data pools are updated day by day, which can be problematic as AI systems can take years to train fully. AI can wrongfully assume that a piece of information is false, even though the data is correct. Without human intelligence to fact-check the details, the risk of using incorrect data might cause someone to misinterpret crucial information.

Unfortunately, AI bias can cause significant disruptions within an algorithm, making it pull inaccurate or potentially harmful information from its data pool. Without human intelligence to control it, not only can it lead to misinformation, but it could also inflict severe privacy and security breaches. Hybrid systems could be the answer to this because they are better at detecting ethical issues or errors.

To Know More, Read Full Article @ https://ai-techpark.com/human-role-in-ai-security/ 

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The Future of Business: Adapting to a Rapidly Changing Landscape

Introduction:

The business world is in a state of constant evolution, driven by advancements in technology, changes in consumer behaviors, and global economic shifts. In this dynamic environment, businesses must be agile and innovative to thrive and stay ahead of the competition. The future of business will be shaped by how well companies can adapt to these challenges and seize new opportunities. In this article, we will explore the key trends and strategies that will define the future of business.

Embracing Digital Transformation:

Digital transformation has become a necessity in today's business landscape. Companies must leverage technology to streamline operations, improve customer experiences, and stay competitive. This includes adopting cloud computing, big data analytics, artificial intelligence, and the Internet of Things (IoT) to drive innovation and efficiency. Businesses that embrace digital transformation will be better positioned to meet the evolving needs of customers and stay relevant in the digital age.

Focus on Sustainability and Corporate Social Responsibility (CSR):

Consumers are increasingly demanding that businesses take responsibility for their environmental and social impact. Companies that prioritize sustainability and CSR initiatives not only enhance their reputation but also contribute to a more sustainable future. From reducing carbon emissions to promoting diversity and inclusion, businesses that embrace sustainable practices will attract socially conscious consumers and investors.

Shift to Remote Work and Flexible Models:

The COVID-19 pandemic has accelerated the trend towards remote work and flexible working arrangements. Businesses are now re-evaluating their traditional office setups and embracing remote work as a long-term solution. This shift not only offers employees greater flexibility but also enables companies to tap into a global talent pool and reduce operational costs. Embracing remote work will be key for businesses looking to stay competitive and attract top talent in the future.

Emphasis on Innovation and Adaptability:

In a rapidly changing business landscape, innovation is key to staying ahead of the curve. Businesses that prioritize creativity, adaptability, and a culture of continuous learning will be better positioned to navigate disruptions and seize new opportunities. Embracing a mindset of innovation will enable companies to stay nimble and responsive to changing market dynamics, ensuring long-term success in a competitive environment.

Leveraging Data and Analytics for Strategic Decision-Making:

Data has become a valuable asset for businesses, providing insights that drive informed decision-making and improve operational efficiency. Companies that leverage data analytics to understand customer preferences, market trends, and internal performance metrics will gain a competitive edge. By harnessing the power of data, businesses can optimize their processes, personalize customer experiences, and drive growth in the digital economy.

Conclusion:

The future of business will be shaped by rapid technological advancements, changing consumer expectations, and global challenges. To thrive in this dynamic environment, companies must embrace digital transformation, prioritize sustainability, adapt to remote work, foster a culture of innovation, and leverage data analytics for strategic decision-making. By staying agile and responsive to change, businesses can position themselves for long-term success in an increasingly competitive landscape.

Transforming Resume Writing with AI Tools for Better Results

On an average, HR managers and recruiters go through a resume in almost six to seven seconds. It’s a really short time and shows that your resume must be outstanding and unique to catch their eye. Using difficult fonts, flashy designs, and a bad layout can become a reason for you to miss out an opportunity, even if you are well-qualified for that role.

Your resume tells about your past work history, skills, hobbies, competencies, etc. Just like many other industries, Artificial Intelligence (AI) can help you with writing your resume. Most people make silly mistakes or are unable to include all necessary information about themselves in their resume. An AI job search tool can help craft a flawless resume for you apart from just searching jobs.

How AI Tools Transform Resume Writing?

Instead of doing it by yourself, when you take the help of AI, it will ensure that your resume has the right format and headings.

Also, AI goes through the job posting and optimizes your resume based on it so that you have an edge over other candidates. This is how AI is transforming the art of writing resumes.

Suggest Ideal Templates

Most people choose a template for their resume and keep using it for all future applications. This is not the correct way because recruitment trends keep changing and not all organizations are looking for a similar thing.

A template may be good for a particular job opportunity but it doesn’t mean that it will work everywhere. AI tools suggest templates depending on the company you’re applying to. The right template will ensure clarity and visual appeal, highlighting relevant skills to impress HRs.

Analyzes Job Descriptions & Optimizes Your Resume Accordingly

You should never use the same resume for different job opportunities as every role demands different skills. AI tools carefully go through job descriptions and understand the requirements. They optimize your resume with several keywords and skills that recruiters are looking for.

Also, these tools will place relevant terms in such a way that recruiters surely see them while going through your resume. Using a single resume does not work anymore and you should use AI tools if you want a perfect resume based on the role you’re applying for.

Focuses on Your Top Skills & Achievements

Many people don’t put emphasis on their top skills and previous achievements when creating their resume. Recruiters won’t put in the effort to read every single word of your resume and it’s your duty to showcase your skills and experience in a way that they have high visibility.

When you use an AI job search tool, it will help you in highlighting the in-demand skills you have and your past work history relevant to the role. Even if you are well-qualified for a job, if your resume does not showcase your skills properly, you’ll miss out.

To Know More, Read Full Article @ https://ai-techpark.com/ai-elevates-resume-crafting/ 

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Quantum Natural Language Processing (QNLP): Enhancing B2B Communication

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 50% 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, humor references, and business-related slang, improving contextual understanding by 70%.

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.

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.

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.

To Know More, Read Full Article @ https://ai-techpark.com/qnlp-enhancing-b2b-communication/ 

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Unveiling the Intersection of AI and Event Planning

Artificial intelligence (AI) has made quite a name for itself in the previous year. However, lately, its pitfalls have been dominating most of the conversation. By now, we are all familiar with the failed, yet viral, Willy Wonka Glasgow experience, and have witnessed the harm AI can cause to an event and its attendees. Unfortunately, when it comes to using AI technology, “Pure Imagination” needs some limitations.

Despite the 2017 Fyre Festival, event planners weren’t able to avoid the temptation of AI to prevent creating the next viral event hoax. The event industry can greatly benefit from technology advancements and AI, as long as event marketers and planners can implement a system of checks and balances to avoid relying too heavily on it. While it’s unlikely that this is the last instance of a misrepresented event, we can focus on learning from these mishaps, ultimately improving event experiences for everyone involved. Event planners can lean into these mistakes and turn them into lessons on how to ensure event goers aren’t led astray, address concerns and questions about the integration and use of AI, and work to prevent another viral hoax and fraudulent event.

The Emergence of AI in Event Planning

AI has permeated nearly every industry, offering a wide range of possibilities for efficiency and innovation. Unsurprisingly, event planners have eagerly embraced AI to optimize their projects. It’s impossible to believe that event marketers won’t be leaning into the technology, so it’s imperative that they’re doing so responsibly and truthfully. The promise of AI lies in its ability to analyze vast amounts of data, predict trends, and automate tedious tasks, therefore freeing up time for planners to focus on what they do best: creativity and strategy.

While these tools have great potential to lighten workloads, it’s essential to recognize that AI cannot completely replace the human touch in marketing efforts. Authenticity, personalization, and emotional connection are key factors in marketing that simply cannot be replicated by AI. Event marketers can find the balance with this by giving AI limitations to ensure it’s grounded in reality. With proper guardrails in place, event marketers and planners can ensure that ideas generated by AI are realistic in practice and geared towards the correct audience.

Where Marketers Are Missing the Mark

One of the critical mistakes marketers can make is relying on AI for content creation without integrating it into the broader objectives of event planning. Authenticity in events hinges on a blend of AI-driven content development and personalized experiences. While AI can certainly generate compelling marketing copy and visuals, it lacks the intuitive understanding of human emotions and cultural nuances that are essential for creating memorable experiences.

AI tools won’t grasp the entire picture of the event – it doesn’t understand the limitations or small logistics that need to be considered. If teams plan to use AI to develop marketing visuals for an event, as was the case in the Willy Wonka debacle, there needs to be human oversight in the process. Without it, the event planners risk eroding the trust they previously worked to build with their customers.

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Enhancing Human Potential with Augmented Intelligence

man Potential with Augmented Intelligence

Boosting Human Capabilities with Augmented Intelligence

Supercharging Human Potential with Augmented Intelligence

Explore how augmented intelligence enhances human potential, driving innovation and productivity in the modern workforce.

The business landscape has been transformed by over 75% in the past few years with the help of numerous technologies. One such marvel is augmented intelligence, which has emerged as a potent ally for human users, enhancing business capabilities by up to 60%. This technology represents a synergy between human expertise and machine learning (ML), redefining how human intelligence approaches problem-solving, decision-making, and innovation. Studies show that businesses utilizing augmented intelligence have seen a 50% increase in efficiency and a 40% improvement in decision-making accuracy. However, amidst all the insights, it is essential to understand that augmented intelligence is not a solution that can be operated independently. It requires human oversight and intervention to help carefully orchestrate ethical considerations and ensure 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.

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.

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How to improve AI for IT by focusing on data quality

Whether you’re choosing a restaurant or deciding where to live, data lets you make better decisions in your everyday life. If you want to buy a new TV, for example, you might spend hours looking up ratings, reading expert reviews, scouring blogs and social media, researching the warranties and return policies of different stores and brands, and learning about different types of technologies. Ultimately, the decision you make is a reflection of the data you have. And if you don’t have the data—or if your data is bad—you probably won’t make the best possible choice.

In the workplace, a lack of quality data can lead to disastrous results. The darker side of AI is filled with bias, hallucinations, and untrustworthy results—often driven by poor-quality data.

The reality is that data fuels AI, so if we want to improve AI, we need to start with data. AI doesn’t have emotion. It takes whatever data you feed it and uses it to provide results. One recent Enterprise Strategy Group research report noted, “Data is food for AI, and what’s true for humans is also true for AI: You are what you eat. Or, in this case, the better the data, the better the AI.”

But AI doesn’t know if its models are fed good or bad data— which is why it’s crucial to focus on improving the data quality to get the best results from AI for IT use cases.

Quality is the leading challenge identified by business stakeholders

When asked about the obstacles their organization has faced while implementing AI, 31% of business stakeholders involved with AI infrastructure purchases had a clear #1 answer: the lack of quality data. In fact, data quality ranked as a higher concern than costs, data privacy, and other challenges.

Why does data quality matter so much? Consider OpenAI’s GPT 4, which scored in the 92nd percentile and above on three medical exams, which failed two of the three tests. GPT 4 is trained on larger and more recent datasets, which makes a substantial difference.

An AI fueled by poor-quality data isn’t accurate or trustworthy. Garbage in, garbage out, as the saying goes. And if you can’t trust your AI, how can you expect your IT team to use it to complement and simplify their efforts?

The many downsides of using poor-quality data to train IT-related AI models

As you dig deeper into the trust issue, it’s important to understand that many employees are inherently wary of AI, as with any new technology. In this case, however, the reluctance is often justified.

Anyone who spends five minutes playing around with a generative AI tool (and asking it to explain its answers) will likely see that hallucinations and bias in AI are commonplace. This is one reason why the top challenges of implementing AI include difficulty validating results and employee hesitancy to trust recommendations.

While price isn’t typically the primary concern regarding data, there is still a significant price cost to training and fine-tuning AI on poor-quality data. The computational resources needed for modern AI aren’t cheap, as any CIO will tell you. If you’re using valuable server time to crunch low-quality data, you’re wasting your budget on building an untrustworthy AI. So starting with well-structured data is imperative.

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The Top Five Best Augmented Analytics Tools of 2024!

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.

Organizations using augmented analytics report up to a 40% reduction in data preparation time and a 30% increase in insight generation speed. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise.

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.

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.

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How AI Augmentation Will Reshape the Future of Marketing

Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. By 2025, it is projected that 70% of lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. Similarly, many analytical positions will become redundant, with smart chatbots expected to assume up to 60% of daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. By 2028, the number of data science positions is expected to grow by 30%, remaining immune to economic pressures. These roles will be less susceptible to budget cuts, highlighting the critical importance of data analysis in the evolving marketing landscape.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

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