Hi Ashok, welcome to our AiThority Interview Series. Being a seasoned technology expert, please tell us about your journey so far.  

Thank you for this opportunity to speak with AiThority. I head the AI.Cloud consulting and advisory group globally at Tata Consultancy Services (TCS), the world’s second largest IT services provider. AI.Cloud is a new business unit launched last year as we saw customers reacting to the GenAI hype. The group brings together the best of TCS capabilities and relationships when it comes to the hyperscalers and cloud services with long-term experience in AI/ML across the company. In my role today, I help the AI.Cloud team work across industries to guide companies in adopting an AI-first business strategy, bringing GenAI, cloud, and data into their business value chains.

Prior to AI.Cloud’s creation, I ran the TCS Digital Workplace for 5 years, helping customers implement the future of work and the future of the employment experience. I have been with TCS for over 25 years, joining straight from university, like many TCSers. Working at TCS means being at the forefront of every new trend. When I joined TCS at the turn of the millennium, software was genuinely seen as something that is going to save the world. In my time here, I’ve had the opportunity to be part of every tech wave, the first push to digital, the introduction to mobile, big data, social networking, etc. All these factors have helped to shape business and the internal employee experience.

Who are your customers and how do they leverage your products/ services?  

As the second largest IT services provider in the world, our customers span across every industry, every vertical. Most recently, we announced a deal with Xerox to consolidate its technology services, migrate legacy data centers to the cloud, deploy a cloud-based Digital ERP, and incorporate generative artificial intelligence (GenAI) into operations to help drive sustainable growth.

Customers choose TCS for our domain experience, proven ability to deliver services, deep relationships with the 3 hyperscalers, and growing in-house innovation to make AI relevant for businesses. Our view is that, fundamentally, GenAI represents the exponential tech disruption for knowledge work. However, the true value of this disruption can only be delivered if you have the basics in place. You need cloud storage and services, quality data, large-scale data systems. This is the foundational digital transformation layer. A starting point for our work with organizations today is to help them on their journey to create and manage a hybrid multicloud environment.

With a powerful and responsive IT environment in place, the next step is to start building purpose-built AI models. We encourage clients to prioritize which business functions benefit the most from automation. In every industry, you find specific use-cases around knowledge work, where automation can take the load off humans and enable them to be more strategic, whether that is using AI for drug discovery or for reviewing resumes in an HR department.

You have been with TCS for the past 25 years, as a Tech Leader, what industries do you think would be fastest to adopt Analytics and AI/ML with smooth efficiency?  

The speed and efficiency with which industries adopt these technologies depends on several factors. Broadly, I see industries that are moving forward quickly falling into two categories: those that have historically been advanced in cloud adoption, and those that have the most impactful use-cases, and thus, the most interest and investment in making it happen.

In the first category, industries that tend to be mature on cloud adoption, we see retail, travel, and CPG businesses succeeding in AI implementations. They have already done much of the hard work to build hybrid multicloud environments, host their data in the cloud, and overall ensure that their data houses are in order.

In the second category, we see industries like banking and financial services and healthcare taking advantage of AI. These industries are looking to AI to create major business impact where there is potential to harness huge amounts of data. As a result, they can use large AI investments to leapfrog over legacy issues and get AI in action more quickly.

While some industries will lead – a recent TCS survey identified the industries most likely to complete AI projects as Life Sciences, Communications, Media and Information Services, and Banking, Financial Services, and Insurance – the GenAI era is democratic. There are AI use cases in every industry and GenAI is making it possible for everyone to get involved.

What is the impact of Generative AI on the workplace?  

When we think about conversations around GenAI and the workplace, it’s one of those beautiful things where everyone will underestimate it in the short term and overestimate it in the long term. In general, we see the rollout of GenAI in the workplace happening across three phases of use-cases: Assist, Augment, Transform.

Assist is what’s happening now – in this context, a human uses AI as an assistant to automate some aspects of a task. We see it in marketing, for instance. A marketer can use an AI assistant to get started on a draft for some marketing copy. These small assistants provide value, but they are really a starting point. The real breakthrough comes when AI can solve something completely – when it works alongside a human, not for a human.

Augment is the next phase, where AI is actually doing a lot of the work. This is one of the promises of AI in IT, where 80-90% of issues can be solved by having the right knowledge – something GenAI excels at. The human is still involved but their primary role is to manage the AI, ensure its accuracy, and determine what problems it should be set on.

Transform is the future state where the power of AI means the very definition of work needs to be reimagined. In this phase, every industry rethinks job roles and the value of human creativity comes to the fore.

The Generative AI era has the potential to make or break companies. What factors contribute to the difficulty of understanding and deploying AI for operational effectiveness?  

What we’ve learned from over 300 GenAI deployments so far is that there is very high interest in implementing GenAI but difficulties in scaling the services at the level needed to make a true difference. Our AI for Business Study showed that most executives are excited and optimistic about AI, only 4% have used it to transform their businesses.

There are a few issues that organizations need to overcome to understand and deploy AI for true operational effectiveness:

Accuracy of models – the truth is, we are still in the earlier days when it comes to LLMs and small language models. Organizations need to understand that models are not yet fully accurate and need to have consistent oversight and human management. While today there are many things GenAI can do, there is room for improvement. Organizations should not overestimate what LLMs can do. We have a lot of engineering still to do in this space.

Security – as AI is accessing large amounts of data, data security and governance of course must be a concern. New tools, approaches, and training are required in order to ensure that data is secure no matter where it is being accessed or being used.

Change management – rolling out AI applications requires a new change management mechanism. AI operating at this level in the workspace is a completely new thing for most workers. Leaders and managers must deeply think about the impact of introducing a new application. The human/AI is dynamic and different from the human/tool relationship in the past. We cannot rely on old change management systems to approach an all-new challenge.

To help clients manage these challenges, TCS’ Design for AI practice focuses on how to build human/AI interaction systems, how to involve specialists, and how to turn specialists into creative generalists. We can’t have one size fits all approaches to how AI is adopted by businesses. In the medium to long-term, work will be transformed, companies will change how they hire, how they staff, how they train. The number 1 thing that will distinguish companies from each other in the future is how effectively they have changed and transformed to adopt AI while also embracing the human element.

How should young technology professionals train themselves to work better with Automation and AI-based tools?  

Young professionals – of all kinds, not just technology professionals – need to rethink the types of skills they develop, and how they talk about the skills they have. Remember that technology in the past 100 years was augment and replace: machines replaced human hands, the first generation of software replaced analytics people, etc. But now AI and automation is available to everyone as a skill. So, professionals need to rethink the value they offer. The resumes/CVs of the future will not just be about industries and experience, but what AI platforms someone is skilled at using, and what they can accomplish through AU and automation.

In addition to building expertise around using AI platforms, the other aspect young professionals need to focus on are skills that make humans more human. Do not worry about developing deep domain expertise in a single subject. Instead, read broadly, expose yourself to new thinking, and aim to become a creative generalist – an expert at applying creativity and human ingenuity to any problem.

What significant AI tools has the company developed over the last few years?  

TCS has developed a large range of AI tools across industries and use-cases, from products like TCS Optumera™, an AI-powered retail-connected strategic intelligence platform, to ignio AIOps, a solution that offers class-leading observability and monitoring features that derives actionable insights from machine data to TwinX, an AI-powered digital twins business simulation and experimentation platform.

Most recently, TCS AI.Cloud has launched WisdomNext™, an industry-first GenAI Aggregation Platform. The platform aggregates multiple GenAI services into a single interface, making it possible for organizations to rapidly experiment with and adopt AI capabilities while also lowering costs. The platform is the result of our conversations with customers who want to deploy AI in their businesses but need flexibility and lower barriers to entry. Uniquely, WisdomNext allows customers to compare and contrast vendor, internal, and open-source LLM models in one interface.

What are your predictions for AI/ML and other smart technologies heading beyond 2024?  

1 – language models will flatten in terms of size and capabilities. We are reaching the peak of what large language models can do. As a result, language models will become more accurate, faster, and cheaper. Language models will become a baked-in commodity to products and services, and we will see small language models proliferate

2 – closer correlation of AI with green energy. AI is very energy intensive. As we scale its use in business and society, we will need to further scale energy production to support this ecosystem. As a result, I believe we will see faster adoption of solar and wind energy to supplement legacy energy sources and meet the quickly expanding energy demands of AI.

3 – In 5-10 years, quantum as architecture will arrive and deliver a huge leap in AI value. The massive compute power of quantum will create new utility with AI platforms that are extremely responsive, secure, and powerful.

Thank you Ashok, it was fun!

 

• About Ashok
• About TCS

About Ashok

Ashok, who heads the Advisory & Consulting function of the AI.Cloud unit at TCS. He leads an interdisciplinary group of AI experts, boasting deep domain knowledge, AI and Generative AI engineering, Data Science, and “Design for AI” capabilities. His team excels in helping large organizations design, implement, and adopt both predictive and Generative AI at scale.

About TCS

Tata Consultancy Services (TCS) is a leading global IT services, consulting, and business solutions organization. It offers a wide range of technology and digital transformation services, helping businesses across industries innovate and achieve their goals.

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Data is the foundation of the most prominent AI applications. To be precise and effective, AI models must be trained on a wide range of datasets. To leverage the potential of AI, enterprises must establish a data pipeline that entails the extraction of data from a variety of sources, its transformation into a consistent format, and its efficient storage. To optimize AI models for real-world applications, data scientists conduct numerous experiments to refine datasets accordingly. To provide real-time performance, these applications, which range from personalized recommendation systems to voice assistants, necessitate the rapid processing of large data volumes.

I will take you through 6 different domains:

• Financial org
• Telcos
• Utility
• Auto Maker
• Retail
• Public Sector

Financial institutions detect fraud in milliseconds

American Express, which processes over 8 billion transactions annually, trains and deploys LSTM models using accelerated computing to tackle these issues. These models are useful for fraud detection because they can adapt and learn from fresh data and sequentially analyze abnormalities.

Financial institutions struggle to detect fraud due to the large amount of transactional data that needs speedy processing. Training AI models are also problematic due to the lack of labeled fraud data. Fraud detection data volumes are too huge for traditional data science pipelines to accelerate. This slows processing, preventing real-time data analysis and fraud detection. 

American Express trains its LSTM models faster using GPU parallel computing. GPUs allow live models to process massive transactional data for real-time fraud detection. To secure customers and merchants, the system functions within two milliseconds, 50x faster than a CPU-based design. American Express increased fraud detection accuracy by 6% in certain segments by merging the accelerated LSTM deep neural network with its existing approaches. Accelerated computing can lower data processing expenses for financial companies. PayPal showed that NVIDIA GPUs may save cloud expenses by 70% for big data processing and AI applications by running Spark3 workloads.

The Telcos simplify complex routing operations

Telecommunications companies create massive amounts of data from network devices, client contacts, invoicing systems, and network performance and maintenance. Managing national networks that handle hundreds of petabytes of data daily involves intricate technician routing for service delivery. Advanced routing engines compute millions of times, considering the weather, technician skills, client requests, and fleet dispersal, to maximize technician dispatch. These operations require careful data preparation and enough computational power.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

AT&T, which has one of the nation’s largest field dispatch teams, is improving data-heavy routing operations with NVIDIA cuOpt, which calculates difficult vehicle routing problems using heuristics, metaheuristics, and optimizations. In early experiments, cuOpt delivered routing solutions in 10 seconds, reducing cloud expenses by 90% and allowing personnel to perform more service calls every day. NVIDIA RAPIDS, a package of software libraries that speeds data science and analytics pipelines, accelerates cuOpt, allowing organizations to use local search methods and metaheuristics like Tabu search for continuous route improvement. AT&T is using NVIDIA RAPIDS Accelerator for Apache Spark to improve Spark-based AI and data pipelines. The organization can now train AI models, maintain network quality, reduce customer churn, and detect fraud more efficiently. AT&T is decreasing cloud computing spending for target applications, improving performance, and lowering its carbon footprint with the RAPIDS Accelerator.

Medical researchers condense drug discovery timelines

Medical data and peer-reviewed research publications have exploded as academics use technology to explore the 25,000 genes in the human genome and their effects on diseases. Medical researchers use these publications to restrict their hunt for new medicines. Such a huge and growing body of relevant research makes literature reviews impractical.

Pharma giant AstraZeneca created a Biological Insights Knowledge Graph (BIKG) to help scientists with literature reviews, screen hit rates, target identification, and more. This graph models 10 million to 1 billion complex biological interactions using public and internal datasets and scholarly publications. Data scientists and biological researchers defined criteria and gene features for therapy development gene targeting to narrow down potential genes. A machine learning algorithm searched the BIKG databases for genes with treatable properties listed in the literature. NVIDIA RAPIDS was used to decrease the gene pool from 3,000 to 40 target genes in seconds, a task that previously took months. By using accelerated computers and AI, pharmaceutical companies and researchers may finally leverage the massive medical data sets to produce breakthrough treatments faster and safer, saving lives.

Read AI In Data Analytics: The 10 Best Tools

Utility Firms Build Clean Energy’s Future

Energy sector shifts to carbon-neutral sources are widely promoted. Over the past decade, the cost of capturing renewable resources like solar energy has dropped, making it easier than ever to move toward a clean energy future.

Integrating clean energy from wind farms, solar farms, and household batteries has complicated grid management. Grid management is more data-intensive as energy infrastructure diversifies and two-way power flows are required. New smart grids must handle high-voltage vehicle charging locations. Distribution of stored energy sources and network usage changes must also be managed. Utilidata, a leading grid-edge software business, and NVIDIA developed Karman, a distributed AI platform for the grid edge, employing a bespoke Jetson Orin edge AI module. This special chip and platform in power meters turns them into data gathering and control stations that can handle thousands of data points per second.

Karman processes real-time, high-resolution meter data from the network edge. This lets utility firms analyze system status, estimate usage, and integrate distributed energy resources in seconds. Inference models on edge devices allow network operators to quickly identify line defects to predict outages and do preventative maintenance to improve grid reliability. Karman helps utilities create smart grids using AI and fast data analytics. This permits tailored, localized electricity distribution to accommodate variable demand patterns without major infrastructure modifications, making grid modernization more cost-effective.

Read: 10 AI In Energy Management Trends To Look Out For In 2024

Automakers Make Self-Driving Cars Safer, More Accessible

Automakers want self-driving cars that can identify objects and navigate in real-time. This involves high-speed data processing, including feeding live cameras, lidar, radar, and GPS data into AI models that make road safety navigation decisions. Multiple AI models, preprocessing, and postprocessing make the autonomous driving inference pipeline difficult. These processes were traditionally done by CPUs on the client side. This might cause severe processing speed bottlenecks, which is unacceptable for a safety-critical application.

Electric vehicle manufacturer NIO added NVIDIA Triton Inference Server to its inference pipeline to improve autonomous driving workflows. Inference-serving, open-source NVIDIA Triton uses multiple frameworks. NIO centralized data processing operations to reduce latency by 6x in some essential areas and enhance data throughput by 5x.

Retailers Forecast Demand Better

Walmart’s data science team constructed stronger machine learning algorithms to tackle this massive forecasting task, but the computing environment started to fail and produce erroneous findings. Data scientists had to delete characteristics from algorithms to finish them, the company found. Walmart used NVIDIA GPUs and RAPIDs to improve forecasting. A forecasting algorithm with 350 data variables predicts sales across all product categories for the company. These include sales statistics, promotional activities, and external factors like weather and the Super Bowl that affect demand.

Data processing and analysis are essential for real-time inventory adjustments, customer personalization, and price strategy optimization in retail. Larger retailers with more products have more sophisticated and compute-intensive data processes. Walmart, the world’s largest retailer, used accelerated computing to increase forecasting accuracy for 500 million item-by-store combinations across 4,500 shops.

Walmart improved prediction accuracy from 94% to 97%, eliminated $100 million in fresh produce waste, and reduced stockout and markdown scenarios with advanced algorithms. GPUs ran models 100x faster, finishing projects in four hours that would have taken weeks on a CPU.

Public Sector Prepares for Disasters

Public and corporate companies use immense aerial image data from drones and satellites to predict weather, follow animal movements, and monitor environmental changes. This data helps researchers and planners make better decisions in agriculture, disaster management, and climate change. If it lacks location metadata, this imagery is less useful.

A federal agency collaborating with NVIDIA sought a solution to automatically locate photos without geolocation metadata for search and rescue, natural disaster response, and environmental monitoring. Like finding a needle in a haystack, pinpointing a small location in a bigger aerial photograph without information is difficult. Geolocation algorithms must account for image lighting and time, date, and angle variances. A Python-based program was utilized by an NVIDIA solutions architect to overcome this challenge. CPU processing took over 24 hours initially. GPUs parallelized hundreds of data processes in minutes, compared to a CPU’s few. The application was 1.8 million x faster after switching to CuPy, an open-source GPU-accelerated library, producing results in 67 microseconds.

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Introduction

Netflix is one of the examples of a machine learning application while AlphaGo DeepMind is Google’s Deep Learning.

The phrases artificial intelligence (AI), machine learning, and deep learning have become increasingly commonplace, even outside of data science. The two terms are often used synonymously. While they share some common ground, these phrases signify different things when discussing autonomous vehicles.

In the broader context of artificial intelligence, deep learning may be thought of as a subset of machine learning. Artificial intelligence (AI) would be at the center, followed by machine learning and finally deep learning, all of which would overlap. To put it another way, artificial intelligence (AI) is not the same thing as deep learning.

Let’s compare ML/DL companies

Other Distinctive Features of Deep Learning versus Machine Learning

Without being explicitly programmed, Machine Learning allows computers to learn from data using algorithms to complete a task. Deep Learning employs an intricate network of algorithms meant to mimic the human brain. Unstructured data may now be processed, including documents, photos, and text.

Read: What Is Augmented Reality?

As we saw, deep learning is a special case of machine learning, and both are branches of AI. Deep learning is often equated with traditional machine learning. Although they are connected, there are some distinctions between the two.

Let’s talk it over!

• A specific type of machine learning is known as “deep learning”. The field of artificial intelligence deals with machine learning.
• When it comes to drawing judgments and conducting analyses, deep learning algorithms rely on their neural networks.
• While models trained using machine learning can improve their performance on certain tasks, they still need human supervision.
• ML can train on smaller data sets, while DL requires large amounts of data.
• ML requires more human intervention to correct and learn, while DL learns on its own from the environment and past mistakes.
• Since deep learning attempts to mimic the functioning of the human brain, the ANN’s structure is far more intricate and interwoven.
• Simpler structures, such as decision trees or linear regression, are used in machine learning algorithms. Since deep learning attempts to mimic the functioning of the human brain, the ANN’s structure is far more intricate and interwoven.
• For difficult issues that require extensive data, machine learning is not as effective.
• ML makes simple, linear correlations while DL makes non-linear, complex correlations.
• Artificial neural networks are the backbone of deep learning systems. Structured data is a prerequisite for most machine learning algorithms.

Nutshell

Machine learning is often confused with deep learning, and vice versa.

Both deep learning and supervised learning are closely related subfields in artificial intelligence. If there is one thing we hope you take away from this piece, it’s that deep learning is a subset of machine learning. The purpose of machine learning is to train computers to increasingly function with minimal human input. Optimizing computers’ cognitive and behavioral processes in ways that mimic the human brain is the focus of deep learning. Spending more time understanding m

Machine learning and deep learning will set you apart from the competition.

New opportunities for machine advancement arise as AI continues to improve. Both Deep Learning and Machine Learning fall under the umbrella term “Artificial Intelligence,” yet they are distinct fields in and of themselves. Machine Learning and Deep Learning are both specialized algorithms that can complete a range of different jobs, each with its own set of benefits. While deep learning doesn’t require much assistance thanks to its basic emulation of human brain workflow and understanding of the context, machine learning algorithms still require some human assistance to analyze and learn from the provided data and arrive at a final decision.

Read the Latest blog from us: AI And Cloud- The Perfect Match

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What Is an Intelligent Virtual Assistant (IVA)?

By 2025, the virtual assistant market size is expected to grow to $25.63 billion.

An intelligent virtual assistant (IVA) is a piece of conversational software that is driven by AI that employs analytics and machine learning to have natural-sounding conversations with users to aid them in locating information, performing an action, or finishing a job. Information gleaned from databases, client histories, connected apps, and prior contacts is used by IVAs to tailor their chats to each user.

Using natural language understanding (NLU), the system can have more nuanced conversations with consumers via digital and audio channels, answering more questions and fulfilling more requests than a chatbot could. IVAs can speak with users in a variety of languages and translate what they say.

The Intelligent Virtual Assistant (IVA) is a chatbot powered by AI that can provide customized replies to each user based on their profile data, prior interactions, and geographic location, all while drawing on the company’s knowledge base and human expertise.
The Intelligence Assistant works as part of the Intelligence Unit’s tactical and administrative intelligence capabilities to effectively identify issues and risks across all aspects of local trading standards operations.

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With the advent of personal computers and smartphones, the World Wide Web is now literally at our fingertips.

In the last ten years, we’ve seen the proliferation of “smart” technology, from LEDs to smart cars to CCTVs to smart bulbs. Along with this, people have grown accustomed to using automated vehicles and urban areas.

What Is IoT?

The term “Internet of Things” (IoT) refers to a network of “things” that are equipped with electronics, software, and network connectivity so that they may share data with other devices and systems online. These gadgets vary from the commonplace to the highly specialized. IoT has rapidly risen in prominence over the past several years to become one of the most consequential innovations of our time. Now that everything from kitchen appliances to vehicles to thermostats to baby monitors can be connected to the internet via embedded devices, there is no longer any barrier to the flow of information among humans, computers, and the physical world.

By 2024, there will be more than 43 billion devices online, all contributing to the creation, distribution, and utilization of information.

So, here’s a rundown of a few of the most important trends that could influence our approach to these gadgets in the future year.

Read: Alteryx Launches New Alteryx AiDIN Innovations to Fuel Enterprise-wide Adoption of Generative AI

10 Steps Towards AIoT

1. AI and IoT technology enable accurate communication through embedded sensors, allowing robots to quickly adapt to new settings. This streamlines manufacturing and saves money.
2. Wearables, such as fitness trackers, smartwatches, panic buttons, remote monitoring systems, GPS trackers, and music systems, are now prevalent in the AI landscape. These devices are vital to the IoT ecosystem and provide reliable data via smart device IoT apps.
3. A smart city includes smart traffic management, parking, trash management, policing, government, and other issues. The Internet of Things for smart cities transforms how cities run and provide public services like transportation, healthcare, and lighting. Smart cities may be futuristic and have much to cover.
4. IoT AI analyzes constant data streams and finds patterns. Machine learning and AI can also predict operation circumstances and identify parameters that need to be changed for optimal results. Thus, intelligent IoT reveals which procedures are redundant and time-consuming and which can be optimized. Google uses AI and IoT to lower data center cooling costs.
5. IoT and AI enable businesses to quickly process and analyze data to generate new products. Rolls Royce aims to use AI for IoT-enabled aviation engine repair. This method will help identify trends and operational insights.
6. IoT devices include smartphones, high-end computers, and sensors. Low-end sensors in the most typical IoT ecosystem generate massive amounts of data. AI-powered IoT ecosystems review and summarize device data before sharing it. It simplifies massive data sets and connects many IoT devices. This is scalability.
7. Self-driving cars are the greatest AI+IoT system in real life. These autos can predict pedestrian movements and recommend cognitive sensing machine actions. It helps determine the best driving speed, time, and route.
8. AIoT is used in car maintenance and recalls. AIoT can detect part failure and perform service checks by combining data from recalls, warranties, and safety agencies. The manufacturer increases customer trust and loyalty as vehicles become more reliable.
9. Quality healthcare aims to reach all communities. No matter the size or sophistication of healthcare systems, doctors are under more time and task strain and seeing fewer patients. Providing high-quality healthcare while managing administrative burdens is difficult.
10. Retail analytics uses camera and sensor data to track and forecast customer behavior in a physical store, such as checkout times. This helps determine staffing levels and boost cashier productivity, enhancing customer happiness.

Recommended AI News: Cloudflare’s R2 Is the Infrastructure Powering Leading AI Companies

Conclusion

The Internet of Things (IoT) is a popular term this decade that refers to the rapidly expanding systems of interconnected, networked, and communicative physical objects.  AI and IoT enable firms to assess, predict, and automate all types of hazards for quick response. This helps them manage financial loss, personnel safety, and cyber dangers.

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Heard about a robot mimicking a person?

Heard about conversational AI creating bots that can understand and respond to human language?

Yes, those are some of the LLM applications.

Their many uses range from virtual assistants to data augmentation, sentiment analysis, comprehending natural language, answering questions, creating content, translating, summarizing, and personalizing. Their adaptability makes them useful in a wide range of industries.

One type of machine learning model that can handle a wide range of natural language processing (NLP) tasks is the large language model (LLM). These tasks include language translation, conversational question answering, text classification, and text synthesis. What we mean by “large” is the huge amount of values (parameters) that the language model can learn to change on its own. With billions of parameters, some of the best LLMs claim to be.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

Real-World Applications of LLM for Success

• GPT-3 (and ChatGPT), LaMDA, Character.ai, Megatron-Turing NLG – Text generation useful especially for dialogue with humans, as well as copywriting, translation, and other tasks
• PaLM – LLM from Google Research that provides several other natural language tasks
• Anthropic.ai – Product focused on optimizing the sales process, via chatbots and other LLM-powered tools
• BLOOM – General purpose language model used for generation and other text-based tasks, and focused specifically on multi-language support
• Codex (and Copilot), CodeGen – Code generation tools that provide auto-complete suggestions as well as creation of entire code blocks
• DALL-E, Stable Diffusion, MidJourney – Generation of images based on text descriptions
• Imagen Video – Generation of videos based on text descriptions
• Whisper – Transcription of audio files into text

LLM Applications

1. Computational Biology

Similar difficulties in sequence modeling and prediction arise when dealing with non-textual data in computational biology. Producing protein embeddings from genomic or amino acid sequences is a notable use of LLM-like models in the biological sciences. The xTrimoPGLM model, developed by Chen et al., can generate and embed proteins at the same time. Across a variety of activities, this model achieved better results than previous methods. The functional sequences were generated by training ProGen on control-tagged amino acid sequences of proteins by Madani et al. To generate antibody sequences, Shuai et al. created the Immunoglobulin Language Model (IgLM). The model showed that antibody sequences can be controlled and generated.

2. Using LLMs for Code Generation

The generation and completion of computer programs in multiple programming languages is one of the most advanced and extensively used applications of Large Language Models (LLMs). While this section mostly addresses LLMs designed for programming jobs, it is worth mentioning that general chatbots, which are partially trained on code datasets such as ChatGPT, are also finding more and more use in programming. Frameworks such as ViperGPT, RLPG, and RepoCoder have been suggested to overcome the long-range dependence issue by retrieving relevant information or abstracting it into an API specification. To fill in or change existing code snippets according to the given context and instructions, LLMs are employed in the code infilling and generation domain. LLMs designed for code infilling and generating jobs include InCoder and SantaCoder. Also, initiatives like DIDACT are working to better understand the software development process and anticipate code changes by utilizing intermediate phases.

3. Creative Work

Story and script generation has been the primary application of Large Language Models (LLMs) for creative jobs. Mirowski and colleagues present a novel method for producing long-form stories using a specialized LLM called Dramatron. Using methods such as prompting, prompt chaining, and hierarchical generation, this LLM uses a capacity of 70 billion parameters to generate full scripts and screenplays on its own. Co-writing and expert interviews helped qualitatively evaluate Dramatron’s efficacy. Additionally, Yang and colleagues present the Recursive Reprompting and Revision (Re3) framework, which makes use of GPT-3 to produce long stories exceeding 2,000 words in length.

Read: State Of AI In 2024 In The Top 5 Industries

4. Medicine and Healthcare

Similar to their legal domain counterparts, LLMs have found several uses in the medical industry, including answering medical questions, extracting clinical information, indexing, triaging, and managing health records. Understanding and Responding to Medical Questions. Medical question answering entails coming up with answers to medical questions, whether they are free-form or multiple-choice. To tailor the general-purpose PaLM LLM to address medical questions, Singhal et al. developed a specific method using few-shot, CoT, and self-consistency prompting. They combined the three prompting tactics into their Flan-PaLM model, and it outperformed the competition on multiple medical datasets.

5. LLMs in Robotics

The incorporation of LLMs has brought improvements in the use of contextual knowledge and high-level planning in the field of embodied agents and robotics. Coding hierarchies, code-based work planning, and written state maintenance have all made use of models such as GPT-3 and Codex. Both human-robot interaction and robotic task automation can benefit from this method. Exploration, skill acquisition, and task completion are all accomplished by the agent on its own. GPT-4 suggests problems, writes code to solve them, and then checks if the code works. Both Minecraft and VirtualHome have used very similar methods.

6. Utilizing LLMs for Synthetic Datasets

One of the many exciting new avenues opened up by LLMs’ extraordinary in-context learning capabilities is the creation of synthetic datasets to train more targeted, smaller models. Based on ChatGPT (GPT-3.5), AugGPT (Dai et al., 2017) adds rephrased synthetic instances to base datasets. These enhanced datasets go above and beyond traditional augmentation methods by helping to fine-tune specialist BERT models. Using LLM-generated synthetic data, Shridhar et al. present Decompositional Distillation, a method for simulating multi-step reasoning abilities. To improve the training of smaller models to handle specific sub-tasks, GPT-3 breaks problems into sub-question and sub-solution pairs.

Read: The Top AiThority Articles Of 2023

Conclusion

Exciting new possibilities may arise in the future thanks to the introduction of huge language models that can answer questions and generate text, such as ChatGPT, Claude 2, and Llama 2. Achieving human-level performance is a gradual but steady process for LLMs. These LLMs’ rapid success shows how much people are interested in robotic-type LLMs that can mimic and even surpass human intelligence.

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In this post, we’ll take a trip down the rabbit hole and talk about how AI fits into the Web3 environment.

We are on the threshold of a new technological era, and experts predict that AI and machine learning (ML) will soon form the backbone of a vast majority of the world’s software.

According to PwC, artificial intelligence would boost global GDP by 14%, or $15.7 trillion, by 2030.

Database and identity management advancements, along with AI, are further solidifying intelligence as the foundation of today’s software systems.

A Symbiotic Relationship: AI AND WEB 3

Machine learning (ML) is revolutionizing our approach to fundamental tenets of software infrastructure, from cloud computing to networking. Web3, the most recent incarnation of the World Wide Web, is no different in this regard. Machine learning is set to play a crucial role in promoting AI-based Web3 technologies as Web3 becomes more widely used.

The incorporation of AI into Web3 does, however, pose several technological difficulties. Therefore, to liberate the full potential of AI in Web3, we must first identify the barriers to this convergence and develop creative approaches to removing them.

As we go deeper into the decentralized realm of Web3, the issue arises: how can AI adapt to and survive in this new setting, shedding its centralization tendencies?

Centralization has long been the standard for AI-based solutions.

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 List of AI web3 companies

• PARSIQ
• Weld
• Golem
• Spice AI
• Unstoppable Domains
• ChainML
• Kosen Labs

Layers of Web3 Intelligence and How AI Contributes to Them

When talking about AI, ML is essential. The integration of ML to Web3 will permeate the whole Web3 stack. Insights powered by ML may be obtained from three essential Web3 layers.

Intelligent blockchains

To facilitate the decentralized processing of financial transactions, current blockchain systems are concentrating on creating critical distributed computing components. Consensus methods, mempool structures, and oracles are all part of these fundamental building elements.

Just as existing software infrastructure building blocks like storage and networking are becoming more intelligent, the next generation of layer 1 and layer 2 blockchains (companion and base) will contain ML-driven features.

Intelligent protocols

Using smart contracts and protocols, the Web3 stack may also incorporate ML capabilities. DeFi is the best example of this pattern.

Defi automated market makers (AMMs) or loan protocols with smarter logic based on ML models are on the horizon. Imagine, for instance, a lending protocol that employs a smart score to distribute funds from several wallets.

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Intelligent dApps

One of the most promising Web 3.0 approaches to incorporating ML-driven functionality quickly is the creation of decentralized apps (dApps).

There has been and will be a growing pattern of this in NFTs. The next generation of NFTs will evolve from simple pictures to interactive objects. These NFTs could be able to modify their actions depending on the owner’s emotional state.

Autonomous Agents

By supplying Autonomous Agents with real-time data and a set of established rules, Web3 platforms can improve the efficacy of smart contracts.

In addition, these agents may carry out transactions, make agreements, and deliver individualized care. Using such agents automates labor-intensive tasks, reduces the need for middlemen, and benefits Web3 as a whole.

Personalization

AI plays a crucial role in the Web3 environment in creating personalized user experiences by analyzing data, analyzing interaction patterns, and analyzing preferences. To improve several aspects of Web3 platforms, AI makes use of collaborative and content-based filtering methods to generate individualized suggestions.

By catering material and interactions to each user’s specific interests, this type of customization boosts user participation in Web3’s decentralized ecosystem while also improving the efficiency of content discovery and curation.

Insights & Analytics

Incorporating artificial intelligence techniques like machine learning and natural language processing, Web3 networks can quickly handle and evaluate massive amounts of data.

This gives consumers the ability to better comprehend decentralized dynamics and navigate the environment through the use of predictive analytics, sentiment analysis, and tailored suggestions.

Safety & Confidentiality

Using cutting-edge AI methods, Web3 ecosystems can enhance cybersecurity and protect user data privacy. Artificial intelligence models can sift through mountains of information in search of security flaws, bad actors, and outliers.

Cybersecurity risks like phishing and distributed denial of service (DDoS) assaults can be thwarted using machine learning algorithms. AI improves users’ faith in Web3 platforms and apps by proactively securing them.

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FAQs: Web3 And AI

• What are the trends and future predictions of the Web3 wallet?

Blockchain will be utilized in the future for a wide range of tasks, including supply chain management and personal data security. Decentralized Finance (DeFi): One of the most popular Web3 ideas is DeFi. It enables you to take charge of your money and stop depending on conventional institutions.

• How does web3 work?

Cryptocurrency is used by a Web3 protocol to encourage individual users worldwide to manage the platform. By engaging in direct transactions with other peers on the network, Web3 users may take advantage of the technology to monetize their goods and services.

• Are web3 and Metaverse the same thing?

A decentralized version of the internet is envisioned by Web 3.0. Virtual environments that allow for online social interaction through digital avatars are referred to as the “metaverse”. We’re going to see more metaverse settings utilizing web3 technologies as they evolve.

• How is web3 different from the internet?

Web3, an improved version of the World Wide Web, is the next generation of the internet. Because its foundation is made up of decentralized technologies like peer-to-peer networks and blockchain, it is often referred to as the “semantic Web” or the “decentralized Web.”

• How can Web3 technology be used to improve data privacy and security?

A key component of Web3 is optimizing data privacy and security. Web3 makes use of blockchain technology to offer people ownership over their data. Users can have transparency over their identities and data records thanks to this peer-to-peer network.

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Top Large Language Model (LLM) APIs

As natural language processing (NLP) becomes more advanced and in demand, many companies and organizations have been working hard to create robust large language models. Here are some of the best LLMs on the market today. All provide API access unless otherwise noted.

1. AWS

A wide variety of APIs for large language models are available on Amazon Web Services (AWS), giving companies access to state-of-the-art NLP tools. These APIs allow enterprises to build and deploy big language models for many uses, including text creation, sentiment analysis, language translation, and more, by utilizing AWS’s vast infrastructure and sophisticated machine learning technology.

Scalability, stability, and seamless connection with other AWS services distinguish AWS’s massive language model APIs. These features enable organizations to leverage language models for increased productivity, better customer experiences, and new AI-driven solutions.

2. ChatGPT

Among the most fascinating uses of LLMs, ChatGPT stands out as a chatbot. With the help of the GPT-4 language model, ChatGPT can hold discussions with users in a natural language setting.ChatGPT is one-of-a-kind because it can assist with a wide range of chores, answer questions, and hold interesting conversations on a wide range of topics because of its multi-topic training. You may swiftly compose an email, produce Python code, and adjust to various conversational styles and settings with the ChatGPT API.

The underlying models can be accessed through the API provided by OpenAI, the company that developed ChatGPT. To illustrate the point, the following is a sample API call to the OpenAI Chat Completions.

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3. Claude

Claude, developed by Anthropic, is an AI helper of the future that exemplifies the power of LLM APIs. To harness the potential of massive language models, Claude provides developers with an API and a chat interface accessible via the developer console.

You can use Claude for summarizing, searching, creative and collaborative writing, question and answer, coding, and many more uses. Claude has a lower risk of producing damaging outputs, is easier to converse with, and is more steerable than competing language models, according to early adopters.

4. LLaMA

When discussing LLMs, it is important to highlight LLaMA, an acronym for “language learning and multimodal analytics,” as an intriguing approach. Meta AI’s development team created LLaMA to solve the problem of language modeling with limited computational resources.

LLaMA’s ability to test new ideas, validate others’ work, and investigate new use cases with minimal resources and computational power makes it particularly useful in the large language model area. To achieve this, it employs a novel strategy for training and inferring models, making use of transfer learning to construct new models more rapidly and with less input data. As of this writing, the API can only process requests.

5. PaLM

You should look into Pathways Language Model (PaLM) API if you are interested in LLMs. Designed by Google, PaLM offers a secure and user-friendly platform for language model extensions, boasting a compact and feature-rich model.

Even better, Pathways AI’s MakerSuite includes PaLM as one component. Prompt engineering, synthetic data generation, and custom-model tuning are just a few of the upcoming features that this user-friendly tool will offer, making it ideal for rapid ideation prototyping.

Conclusion

Exciting new possibilities may arise in the future thanks to the introduction of huge language models that can answer questions and generate text, such as ChatGPT, Claude 2, and Llama 2. Achieving human-level performance is a gradual but steady process for LLMs. These LLMs’ rapid success shows how much people are interested in robotic-type LLMs that can mimic and even surpass human intelligence.

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Large Language Models (LLM) and Generative AI are two models that have become very popular in the ever-changing world of artificial intelligence (AI). Although they are fundamentally different, architecturally distinct, and application-specific, both methods enhance the state of the art in natural language processing and creation. Explore the features, capabilities, limitations, and effects of LLM and Generative AI on different industries as this essay dives into their intricacies.

Large Language Models (LLM)

A subset of artificial intelligence models known as large language models has been trained extensively on a variety of datasets to comprehend and produce text that is very similar to human writing. The use of deep neural networks with millions—if not billions—of parameters characterizes these models as huge in scale. A paradigm change in natural language processing capabilities has been recognized by the advent of LLMs such as GPT-3 (Generative Pre-trained Transformer 3).

LLMs work by utilizing a paradigm that involves pre-training and fine-tuning. The model acquires knowledge of linguistic patterns and contextual interactions from extensive datasets during the pre-training phase. One example is GPT-3, which can understand complex linguistic subtleties because it was taught on a large corpus of internet material. Training the model on certain tasks or domains allows for fine-tuning, which improves its performance in targeted applications.

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Generative AI

In contrast, generative AI encompasses a wider range of models that are specifically built to produce material independently. Although LLMs are a subset of Generative AI, this field encompasses much more than just text-based models; it also includes techniques for creating music, images, and more. Generative AI models can essentially generate new material even when their training data doesn’t explicitly include it.

The Generative Adversarial Networks (GANs) family is a well-known example of Generative AI. Adversarial training is the foundation of GANs, which also include a discriminator network and a generator network. Synthetic data is produced by the generator, and its veracity is determined by the discriminator. Content becomes more lifelike as a result of this adversarial training process.

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LLM Vs Generative AI

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AI – A Bird’s Overview

The term “artificial intelligence” (AI) is used to describe a wide range of computer programs that attempt to simulate human intelligence to solve complicated problems and improve over time.

To solve difficulties, AI software can mimic human brain activity. The end objective is to create a smart machine that can handle difficult workloads. There is a vast potential market for AI. To simulate human judgment, AI integrates many technologies into a system. Artificial intelligence can process any kind of data, including those that are only partially organized.
To learn, reason, and self-correct, AI systems make use of logic and decision trees.

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ML – A Bird’s Overview

Machine learning falls outside of the realm of artificial intelligence, which is concerned with creating machines that are capable of human-level cognitive function. The goal of machine learning is to train a computer to carry out a task by itself, producing reliable results by recognizing patterns. For example, you may ask your Google Nest, “How long is my commute today?”

This graph, sourced from Deloitte, illustrates how important data transformation is for ML. The proper format of data is essential for the complete deployment of ML. Machine learning is only useful with massive volumes of data, which are tedious to gather, organize, and keep. Most people who took the survey think that developing models, transforming data, and managing and monitoring models are the most labor-intensive parts of artificial intelligence.

You may ask a machine how long it will take to go to work, and it will give you an estimate. The end aim here is for the gadget to do something useful for you, something you may have to do manually in the real world.

ML’s inclusion in the larger system is not intended to improve its functionality in this scenario. Foreseeing traffic volume and density, for instance, may require training algorithms to monitor real-time transit and traffic data. However, this analysis is restricted to learning from the data to achieve optimal performance on the targeted task and discovering trends based on how accurate the prediction was.

You may think of ML as a subset of both AI and Data Science. Siri from Apple, Google Assistant, Tesla self-driving vehicles, Amazon Alexa, etc. are all good instances of artificial intelligence. Google’s search engine, Twitter’s sentiment analysis, stock prediction, news classification, etc., are all excellent instances of machine learning in action.

The range of uses for machine learning is somewhat small. Models for future outcomes are generated by ML’s self-learning algorithms. Structured and semi-structured data are required for ML. Machine learning (ML) systems use statistical models for learning and correction and may improve themselves with additional data.

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