Рубрика: AI

Indian IT giants TCS, Infosys, HCLTech, and Wipro recently released their quarterly results and many of them expressed low confidence regarding generative AI and did not disclose the exact revenue figures contributed by generative AI.

Though the Indian IT companies haven’t made any big announcements about the impact of generative AI on the revenue growth, they are quite vocal about integrating generative AI into their operations. Their biggest investment is in training their own employees in the field of GenAI.

How are Indian IT companies training their employees?

Last year, Infosys announced that it plans to train 100,000 employees, focusing on theoretical and practical aspects through partnerships with GenAI leaders. TCS is training over 150,000 employees, emphasising foundational skills in collaboration with tech giants.

“We have over 100,000 GenAI-ready employees today, and we are now investing in deepening their expertise further with the exciting new technology,” said TCS’ executive vice president and global head – human resource, Milind Lakkad, at the recent earnings call.

While the numbers are impressive, questions arise about the depth of skills, specialisation options, and real-world application integration. This trend reflects the industry-wide race to build a skilled GenAI workforce, underscoring the growing demand for expertise in this transformative field.

Through internal training initiatives such as the Infosys Learning Hub and specialised Skill Academies, Infosys trains its employees to delve into a wealth of AI-related courses, covering fundamental concepts to advanced applications. Mentorship programs further enhance learning through personalised guidance from seasoned AI practitioners.

Leveraging technology-enabled learning, Infosys integrates AI simulation platforms and gamified courses, ensuring practical skill development in controlled environments. Meanwhile, TCS, HCL Tech, and Wipro also employ distinct methods to train their employees, like Infosys.

TCS prioritises innovation with research centres, whereas HCL Tech bridges academic-industry gaps, and Wipro emphasises domain-specific training via HOLMES. Despite differences, all invest in job-specific training and leadership development.

The distinct focus areas of these IT giants contribute to a comprehensive landscape where generative AI is harnessed for diverse applications, from enhancing customer experiences to automating compliance tasks.

Despite employing a substantial workforce compared to other major tech giants, they underutilise their highly-trained employees, limiting the potential for innovations. Although they are using generative AI to train their employees, they are not being trained for the actual purpose of what AI engineers serve.

Also read: What to expect from Indian IT in 2024?

Are they making use of their workforce in the right way?

OpenAI, Meta, and Google are the foundation model builders. OpenAI, despite its smaller engineering force, has contributed a lot to research and open-source segments. These companies develop models and serve as product and model sellers, offering APIs and applications for developers to integrate and customise.

On the other hand, Infosys, HCL, TCS and Wipro stand out as genAI application developers and service providers. They use these foundational models to produce client solutions, deploying generative AI in diverse applications.

Indian tech giants boast of large and experienced workforces with a proven track record of managing complex projects but still struggle with ambitious projects and limited data access. Although the workforce is huge for these Indian IT companies compared to other big tech companies, the question arises as to why they are not using this workforce for a better solution.

To navigate the evolving tech landscape and compete with Big Tech, Indian IT giants like Infosys, TCS, HCL Tech, and Wipro must undergo a transformative shift in mindset. Moving beyond cost optimisation, they should position themselves as innovation partners, deeply understanding clients’ industries and investing in emerging technologies.

While directing the delicate balance between quantity and quality in India’s AI landscape, addressing the current skill gap and aligning the focus towards innovation is imperative. These AI engineers find themselves shouldering the weight of AI services rather than being able to focus on generating innovative AI solutions.

Although India’s extensive pool of AI engineers, notably within companies like TCS and Infosys, offers a strategic advantage in workforce size, there is a pressing need to elevate the skillset and cultivate a culture of innovation.

The post Are Indian IT Companies Producing AI Coolies Instead of AI Engineers? appeared first on Analytics India Magazine.

At the World Economic Forum in Davos, Julie Sweet, Accenture’s chief, announced that the company allocates an annual budget of $1 billion for training its employees in generative AI. According to Sweet, the technology, though at a nascent stage, is moving fast. GenAI is not a fad; rather, it is developing at a rate ten times faster than that of earlier major advances.

Sweet said that in order to better basic education, collaboration with governments is necessary. She also said, “It’s not going to help now, but we need to think 10-20-30 years ahead.” According to her, leadership is the single most important factor that decides whether the business or government successfully uses GenAI or not.

She also said that “you actually have to understand it at a very deep level because it is not that there are millions of use-cases…. but you have to operationalise it.”

Similarly, Arvind Krishna, chairman and CEO of IBM, who was sitting alongside Sweet in the panel, claimed that Gen AI is a rapidly developing technology that is advancing more quickly than earlier major ones.

In order to apply AI, Krishna underlined the significance of reskilling talent because it will create new employment and solve numerous issues. To secure the success of AI, he also emphasised the necessity for businesses and governments to collaborate on reskilling.

Moreover, he suggested controlling use-cases rather than the technology itself. Additionally, he forecast that before the end of the decade, AI in its current form will produce $4 trillion in yearly productivity.

The post Accenture Announce $1 Bn Annual Investment in GenAI Training at Davos 2024 appeared first on Analytics India Magazine.

Meta AI recently introduced MAGNeT, a text-to-audio generation model that promises to enhance how we create and experience sound. This non-autoregressive transformer model operates on multiple audio token streams, enabling rapid and efficient audio generation with a single-stage approach.

Striking a balance between speed and quality, it combines autoregressive and non-autoregressive methods for different parts of the sequence, ensuring optimal results. Leveraging an externally pre-trained model to rank and refine predictions, ensuring to push the boundaries of audio quality and realism.

A remarkable 7x speed increase compared to autoregressive baselines opens up possibilities in music production, sound design for various media projects, and creative exploration of diverse soundscapes. Moreover, its potential for developing accessibility tools for individuals with visual impairments or reading challenges is promising.

Check out the GitHub repository here.

About MAGNeT

Meta AI’s MAGNeT showcases cutting-edge technology in text-to-audio generation and delves into the trade-offs between autoregressive and non-autoregressive models. Through meticulous ablation studies, the researchers have explored the impact of individual components, providing valuable insights into the model’s performance.

To make the model accessible to a broader audience, Meta AI has also introduced a user-friendly Gradio demo. This web interface empowers users to test MAGNeT’s capabilities without coding experience, democratising access to advanced audio generation technology.

Its innovative architecture and advanced techniques set it apart, as the non-autoregressive design predicts masked token spans simultaneously, accelerating the generation process and simplifying the model by employing a single-stage transformer for both the encoder and decoder.

Integrating a custom masking scheduler during training and progressive decoding during inference adds a layer of adaptability, optimising learning and potentially mitigating errors. MAGNeT further distinguishes itself through a novel rescoring method, leveraging an externally pre-trained model for refining predictions and enhancing audio quality.

Comparing it with other top models reveals its strengths in efficiency and quality, making it an appealing choice for applications where rapid audio synthesis is paramount. While models like Jukebox and MuseNet excel in high-fidelity and expressive music generation, MAGNeT’s focus on overall quality and speed positions it uniquely in the domain.

The hybrid version’s combination of autoregressive and non-autoregressive approaches strikes a balance between initial high-quality generation and subsequent rapid parallel decoding. MAGNeT sets a new standard for efficient and high-quality text-to-audio synthesis, opening avenues for advancements in the field.

The post Meta Launches MAGNeT, An Open-Source Text-to-Audio Model for On-the-Go Music Creation appeared first on Analytics India Magazine.

The calendar flipped to 2024 and layoffs have once again began to cause large-scale anxieties. Duolingo, Paytm, Amazon, and Twitch have decided to hand over pink slips to many of their employees. While this trend is likely to grow, we decided to go against the grain and find you something good to cling on to.

Here’s a list of a few lucrative jobs in generative AI.

Principal AR in Generative AI, eBay

The eBay advertising team is seeking a principal applied researcher in generative AI to contribute to the reinvention of ads at eBay. The team focuses on developing innovative ad-tech solutions to enhance ad monetisation and user experience on the platform.

The role involves leading a team of researchers and engineers to apply generative AI in building advanced advertising products, with the ultimate goal of creating a valuable experience for both buyers and sellers, as well as driving performance for advertisers.

In this remote role, the candidate should have a proven track record in applied ML research, a master’s degree in relevant fields, and over seven years of industry experience with strong expertise in generative AI, deep learning, and various related areas such as NLP, recommendation systems, and image understanding.

Head of AI Data Operations, Krutrim

Bhavish Aggarwal’s full stack AI solutions company Krutrim is currently seeking a head of AI data operations for a full-time position in Bangalore. For this role in Ola’s subsidiary, you will oversee all aspects of data annotation and operations for AI model training across text, image, video, and multimodal datasets.

The responsibilities include managing data collection and curation activities, establishing operations for large-scale data collection within the Ola ecosystem, collaborating with engineering leaders, and managing relationships with external data vendors. The ideal candidate should have a strong operations background, extensive project management skills in AI and ML, and a passion for advancing generative AI with data.

The minimum qualification for this position includes a bachelor’s degree in computer science, engineering, or a related field; an advanced degree in operations and management will be preferable. The candidate should have over 10 years of experience in technical data roles for AI, demonstrating a track record of impactful data products and pipelines.

Machine Learning-Software Development Engineer, Accenture

The ML-software development engineer position at Accenture in Bengaluru needs the candidate to have at least 10 years of experience and a bachelor’s degree in technology, engineering, or a quantitative field. The primary tasks include analysing, designing, coding, and testing various components of application code for one or more clients.

The role emphasises the development and implementation of data-driven solutions using generative AI models, involving interactions with client stakeholders to understand AI problems, prioritise use-cases, and define problem statements.

Technical experience requirements include the capacity to develop high-impact thought leadership, extensive use of data-driven techniques such as exploratory data analysis and data pre-processing, proficiency in Python programming for data manipulation, visualisation, and machine learning models, and familiarity with at least one cloud solution (IBM Cloud, Azure, GCP, or AWS).

Tech Lead – Generative AI, Dentsu

The position of tech head-generative AI at Dentsu in Pune involves developing and implementing advanced generative AI models, particularly focusing on text and image generation, and fine-tuning AI. The responsibilities include collaboration with cross-functional teams to understand client requirements, researching emerging trends in generative AI, and evaluating and fine-tuning models for optimal performance.

The tech lead will also collaborate with data scientists and engineers to integrate AI solutions into existing systems, staying updated on AI advancements, and contributing to the company’s technical knowledge base.

The perfect fit for this position should have a minimum of three years of experience in generative AI, a degree in computer science or related fields, a proven track record in implementing generative AI models, strong problem-solving skills, and effective communication abilities.

Technical skills include Python programming, end-to-end understanding of backend workflows, familiarity with chatbots/dialogue flows, and knowledge of NoSQL and unstructured data, TensorFlow, PyTorch, or Keras, basic knowledge of machine learning algorithms, and hands-on experience with cloud platforms such as AWS and Azure for scalable model deployment.

Associate Partner – AI, EPAM Systems

EPAM Systems is looking for an engineering lead with expertise in generative AI and cloud solutions who will spearhead generative AI initiatives for the tech conglomerate. The responsibilities include leading the development and execution of generative AI and LLM projects, ensuring they align with business goals.

The candidate will design and deploy Proof of Concepts (POCs) and Points of View (POVs) across different industry verticals. Besides, the lead should be able to engage with customer CXOs and business unit heads to demonstrate the relevance and impact of generative AI applications.

Requirements for the position include a minimum of 12 years of overall experience, with a strong background in AI/ML and expertise in working with major cloud platforms such as Azure, GCP, and AWS.

Generative AI Analyst, Trust and Safety, Google

The position demands a minimum of four years of experience using analytical tools like SQL and Python. Besides a bachelor’s degree, preferred qualifications include expertise in dealing with abuse, spam, fraud, or malware, as well as experience in product policy analysis and statistical analysis.

The Trust & Safety team focuses on solving significant challenges affecting the safety of Google products, requiring global collaboration to identify and address abuse and fraud cases. The role involves developing and managing cross-functional relationships to enhance processes for resolving policy and quality issues in generative AI products.

The chosen candidate will also establish metrics for generative AI issues, conduct data analyses, and drive project delivery with technical judgement. They will collaborate with various teams, enhance tools, improve workflows, and analyse escalations to identify product risks.

Generative AI Engineer, Gramener

Gramener is looking for generative AI engineers to design and implement advanced foundational models, contributing to the development of AI-driven solutions for high-quality content generation across various media types.

Collaboration with cross-functional teams is crucial, requiring a deep understanding of machine learning, neural networks, and generative modelling techniques. Responsibilities include staying abreast with the latest advancements in generative AI, customising model architectures, preprocessing datasets, optimising model training, fine-tuning hyperparameters, and deploying models into production systems.

The engineer must have a degree in computer science or a related field, proficiency in Python and relevant AI libraries, experience with generative modelling techniques, familiarity with cloud computing platforms, strong problem-solving skills, and effective communication abilities.

The post 7 Generative AI Jobs for You appeared first on Analytics India Magazine.

In the latest episode of Unconfuse Me with Bill Gates, OpenAI chief Sam Altman revealed that his company is led by a bunch of older folks. “It’s not a bunch of 24-year-old programmers,” he added.

“You have a lot in their 30s, 40s, and 50s. It’s not the early Apple, Microsoft, where we were really kids,” fretted Gates, who is in his 60s.

Altman said that somehow it is a bad sign for society. “The best founders have trended older over time. Then in our case, it’s a bit older than the average,” shared Altman, saying that he tracked this during his YC days where companies have gotten older in general.

Is age just a number?

Not just OpenAI, if you look at the majority of the tech companies, the likes of Perplexity AI, Midjourney, Stability AI, Anthropic, Cohere and others, most of them are in their late 30s or early 40s.

The same goes for next generation tech startups, the likes of Humane Ai and Rabbit, who are in their 30s and early 4os, have previously worked in companies like Apple and Baidu, respectively.

“This is a topic that could engender endless debate (esp. since team age != founder age),” said the general partner of RRE Ventures, Jason Black, highlighting the trend of older individuals increasingly founding successful companies, especially in the B2B sector.

He said that seasoned entrepreneurs, rather than first-timers, often make the best founders due to their experience and ability to experiment more effectively, a luxury not as accessible to younger entrepreneurs in the past. He also noted that raising significant capital tends to be easier for those over 30.

Additionally, Jason highlighted the complexities in pioneering new technological fields, which often require not just innovative breakthroughs but also new infrastructure and industry expertise. This expertise is frequently found in individuals who have already established notable careers, hence are older.

“While I think these factors influence the trend towards the ‘best’ founders trending older, our entire industry is based on the exceptions. Fortunately, exceptional people are exceptional regardless of their age,” shared Black.

“As a founder in my 30’s I am encouraged by this,” said founder of Passio AI, Dmitriy Starson, saying that most exciting, inspirational, e/acc ideas he is seeing these days are coming from the 30-40 year olds.

“40 is the new 20!! 😎” said founder of GAIM Network, Brady Lewis, saying that he understands the nuances of tech and business much better after spending eight-years in a tech leadership role at Salesforce.

The Rise of Older Tech Founders

Research from the HBR supports this trend, indicating that the average age of successful tech founders is 45 years old. This data suggests that experience is increasingly valued across various industries, not just technology.

A study published last year revealed that business founders aged 50 or older are more likely to introduce significant new products or services compared to younger entrepreneurs.

Further, the study found that for every additional decade in a founder’s age, the likelihood of bringing something new to the market increases by 30%. This is preceded by the study by the Census Bureau and MIT professors published in 2018 challenging the tech industry’s youth bias.

The research also highlighted that the probability of success increases with age. For instance, a 50-year-old founder is 1.8 times more likely to launch a successful company than a 30-year-old. If this continues, will it be likely that the founders in their 50s and 60s build successful companies?

The post OpenAI is Not Built by 24-Year-Old Programmers appeared first on Analytics India Magazine.

Microsoft and Vodafone recently announced a 10-year strategic partnership. The collaboration aims to utilise their strengths in providing digital platforms to over 300 million businesses, public sector organisations, and consumers in Europe and Africa.

Vodafone will invest $1.5 billion over the next decade in cloud and customer-focused AI services developed with Microsoft. The partnership will involve the transformation of Vodafone’s customer experience using Microsoft’s generative AI, scaling Vodafone’s managed IoT connectivity platform, developing new digital and financial services, and revamping its global data center cloud strategy.

Microsoft plans to invest in Vodafone’s managed IoT connectivity platform, which will become a separate business by April 2024. The new company aims to attract partners and customers, fostering growth in applications and expanding the platform to connect more devices.

“We are delighted that together with Vodafone we will apply the latest cloud and AI technology to enhance the customer experience of hundreds of millions of people and businesses across Africa and Europe, build new products and services, and accelerate the company’s transition to the cloud.” said Microsoft chief Satya Nadella.

The collaboration identifies five key areas: Generative AI to enhance customer satisfaction, scaling IoT, accelerating digital growth in Africa, supporting enterprise growth, and facilitating Vodafone’s cloud transformation by modernising its data centers on Microsoft Azure.

M-Pesa, Africa’s top fintech platform, will scale with Microsoft’s assistance, aiming to impact millions through digital literacy and SME support.

“This unique strategic partnership with Microsoft will accelerate the digital transformation of our business customers, particularly small and medium-sized companies, and step up the quality of customer experience for consumers.” said Margherita Della Valle, Vodafone Group chief executive.

The post Vodafone Signs $1.5 billion Microsoft Deal to Unleash Generative AI appeared first on Analytics India Magazine.

A few weeks back, Swaayatt Robots, an Indian autonomous driving company, claimed Level 5 autonomy. The company announced this big achievement through a video, showcasing autonomous driving of the car at night at a toll plaza amid complex traffic dynamics.

The video was still not convincing for many as the company succeeded in the development of Level 5 autonomy when Tesla, General Motors and others were still at Level 2.

To clear the air, AIM did an exclusive interview with Sanjeev Sharma, founder and chief, Swaayatt Robots, who confirmed, “If we look at the demo which was conducted at the toll plaza, the vehicle showed Level 5 capabilities.”

“At night, there were no traffic rules. You could see trucks going zigzag, overtaking our vehicle randomly. There were also trucks parked here and there. Our vehicle arriving at the intersection slows down for the speed breaker and then decides which lane or gate to commit to. He believes this capability falls under level 5 autonomy,” he added.

Moreover, Sharma said their technology is capable of negotiating bi-directional traffic on a single lane. “No other company deals with this. They just came to a complete halt. Our vehicle just took a small detour even when the object was coming from the wrong end. Now, we are able to do this because this intelligence is inherently embedded.”

Furthermore, he added that he leaves it up to the people to decide whether this is level four, level five, or even if they want to quantify level three – they are okay with it. “However, wherever we felt that a claim had to be made, we made the claim, specifically in toll plaza navigation, asserting that this is level 5.”

Tech Behind This

Sharma said that Swaayatt Robots is heavily invested in Reinforcement Learning and Inverse reinforcement learning. He considers Wavye AI to be their toughest competitor, given that Wavye AI also employs reinforcement learning. Moreover, he said that Swaayatt Robot’s current focus is to get rid of as many algorithms as possible.

According to Sharma, other companies are working on developing algorithms for obstacle detection and the intent of the other vehicles. They are developing a technology to eliminate the obstacle detection algorithm in autonomous vehicles. Their focus is on creating a decision-making system with an inherent understanding of the world’s context, without the need for explicit computation to detect various obstacles or road intent.

Recently, a Bengaluru developer converted his modified Maruti Alto K10 into an autonomous vehicle using a second-hand Redmi Note 9 Pro and an open-source fork of Comma.ai’s OpenPilot.

Regarding Comma.ai, Sanjeev mentioned their use of Apprenticeship Learning. “Apprenticeship learning, in terms of reinforcement learning, involves behavioural cloning, where you try to mimic or clone the human expert. This is the approach taken by Comma.ai and other startups, but it’s not scalable – it’s a dead end already.” he said.

Speaking about Tesla he said they use Autoregressive Reinforcement Learning, which could be scalable but a bit doubtful as training and running ARL models can be computationally expensive, requiring powerful hardware and significant resources.

Sharma said that Swaayatt Robots is trying to challenge the three major pipelines of autonomous driving in the “classical sense”, which are perception, localisation and mapping and planning.

“Since 2019, with the advent of Wavye AI, people have been discussing autonomy without relying on maps. Localisation against maps necessitates the use of maps, but we achieved everything without them. We were the first technology to enable vehicles without the reliance on high-definition maps. In 2017, we implemented multi-RL agents without requiring any maps,” he said.

Furthermore he added that they have autonomous vehicles with mounted cameras; hence, they don’t require external data supply. They have pipelines for automatic data labeling and use generative AI to create specific scenarios, though not on the scale of NVIDIA. Their setup includes 8 side-looking cameras, 2 lidar units, and 2 additional cameras on the bumper. We plan to add 4 more cameras to the rear.

What’s Next?

Sharma revealed that the team is actively working on an undisclosed ‘X’ paradigm, intending to showcase how autonomous vehicles will independently acquire various skills. This demonstration is scheduled for the month of February.

Moreover he said that he expects the autonomous driving tech market is projected to be a trillion-dollar industry by 2030, with only a maximum of five companies expected to survive. “Our goal is to secure 25% of the market by 2030 and evolve into a multinational corporation,” he added.

“I believe that by that time, we will have a pan-India presence and will have raised $1 billion. Currently, we have only $3 million in funding, so even if we achieve sophistication in a specific algorithmic framework for a certain problem, it can only be demonstrated on a limited scale.” he concluded.

The post [Exclusive] Indian AI and Robotic Company Confirms Level 5 Autonomy appeared first on Analytics India Magazine.

Ahead of the US Presidential elections 2024, OpenAI has come up with a suite of updates on how to tackle potential abuses, deepfakes and misinformation created by generative AI for a reliable voting process.

“Our tools empower people to improve their daily lives and solve complex problems – from using AI to enhance state services to simplifying medical forms for patients. We want to make sure that our AI systems are built, deployed, and used safely,” read the official blog post.

Firstly, the company is coming up with a new provenance classifier tool for DALL·E-generated images and has exhibited promising early results, as per its blog post. This tool is set to become available for feedback from initial testers, including journalists and researchers.

This is done to foster transparency in image provenance, allowing voters to check which tools were used in making them. The team is also incorporating digital credentials from the Coalition for Content Provenance and Authenticity to encode image details through cryptography.

Secondly, for ChatGPT and its API, the platform prohibits applications for political campaigning and lobbying until their effectiveness for personalized persuasion is understood. Impersonation of real individuals or institutions, discouraging voting, and misrepresenting voting processes are also forbidden to maintain trust and safeguard democratic processes. Users can report violations using the new GPTs for enhanced accountability and user involvement.

In addition, ChatGPT has integrated with real-time global news reporting, delivering users with sources and relevant links, granting voters the ability to independently evaluate and trust the information they receive.

The AI research lab is also collaborating with the National Association of Secretaries of State (NASS), a nonpartisan group for public officials to improve access to authoritative voting information. ChatGPT guides users to CanIVote.org for accurate U.S. voting details, such as polling locations.

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