Рубрика: AI

Oracle has partnered with the Tamil Nadu Skill Development Corporation (TNSDC) to launch a specialised skills development program aimed at empowering over 200,000 students in the state with the latest IT skills.

This collaboration falls under Tamil Nadu’s ambitious skill enhancement initiative, Naan Mudhalvan, which focuses on equipping youth with employment-linked training in cutting-edge technologies.

The program, launched in 2024, targets students across various educational backgrounds, including engineering, arts, and science streams, from more than 900 colleges statewide.

These students will receive training and certification in modern technologies such as Oracle Cloud Infrastructure (OCI), data science, artificial intelligence (AI), machine learning (ML), and blockchain.

“Tamil Nadu, with its burgeoning youth population, is a priority state for us. Through Naan Mudhalvan, we aim to provide a platform for youth to enhance their career prospects,” said Tmt J. Innocent Divya, managing director of TNSDC.

The initiative, already reaching 1.3 million students, aims to leverage Oracle’s expertise to train an additional 200,000 students, opening doors to promising career opportunities.

The training curriculum, integrated into college curricula, will be delivered by experienced educators and industry professionals. Digital learning modules via Oracle MyLearn, the company’s robust training platform, will complement on-campus instruction.

This blended approach ensures comprehensive learning in cloud computing and related domains, tailored to individual learning levels and career aspirations.

Shailender Kumar, senior vice president and regional managing director of Oracle India and NetSuite JAPAC, expressed enthusiasm about the collaboration. “Oracle’s Skills Development Initiative aligns with the Indian government’s digital economy vision. Our certification programs are industry-recognized, enhancing employability and stability for tech professionals,” he said.

Oracle University, renowned for its technology training and certification programs, has already impacted over 3.1 million certified professionals globally.

The post Oracle to Train 200,000 Students in AI and Machine Learning in Tamil Nadu appeared first on AIM.

Stability AI has been a key player in the artificial intelligence (AI) image generator space thanks to its open-source Stable Diffusion models, which set the bar for quality, customization, and speed. Now, the company is adding to its family of models with its most advanced text-to-image generator yet.

On Wednesday, Stability AI launched Stable Diffusion 3 Medium, which the company claims is its "most sophisticated" image generation model. The two-billion-parameter model boasts several upgrades from its predecessors, resulting in higher-quality generations.

Also: How to use Stable Diffusion AI to create amazing images

For example, the new model can overcome typically difficult tasks for image generators, including generating photorealistic images (even of hands and faces) and accurate text without artifacts or spelling errors. It can also adhere to complex prompts and understand spatial relationships, as seen in the image below.

According to the company, Stable Diffusion 3 Medium is a smaller model, making it a good candidate for running on both individual computing systems and enterprise-tier GPUs. Stability AI added that the model is also ideal for customization due to its ability to gather "nuanced details from small datasets."

Also: The best AI image generators of 2024: Tested and reviewed

Stable Diffusion 3 Medium's weights remain open-sourced and accessible to all users with a free non-commercial license via Hugging Face. Those interested in using the commercial model are encouraged to contact Stability AI for licensing information.

Stable Diffusion 3 Medium is available on Stability AI's API, Stable Assistant, the company's chatbot, and Discord via Stable Artisan.

Artificial Intelligence

Are you one of the people who seemingly used the GPT Builder in Microsoft's AI-based Copilot Pro? If so, you can kiss it goodbye. Come July 10, Microsoft will squash the tool permanently — at least for the consumer version of Copilot.

On a new support page, Microsoft said that it will remove the ability to create GPTs starting July 10 and then delete all GPTs — those created by the company and its customers — from July 10 through July 14. In a nod toward privacy, any data associated with or collected by the GPTs also will be gone.

Also: How to use ChatGPT to create an app

Launched just three months ago in mid-March, Copilot's GPT Builder works similarly to OpenAI's ChatGPT Plus GPT Builder. Both tools let you create your own custom GPT chatbots to use yourself or share with others. Creating your own task-specific chatbot this way requires no coding skills or knowledge as the tool talks you through the process.

The difference is that the ChatGPT Plus GPT Builder lets you use your GPTs privately, share them with specific people, or publish them to the GPT store. With Copilot Pro's GPT Builder, you can use them only privately or share them with specific people.

OK, but why kill off the tool after only three months of life? Here's the explanation from Microsoft:

"We are continuing to evaluate our strategy for consumer Copilot extensibility and are prioritizing core product experiences, while remaining committed to developer opportunities," the company said on the support page. "To this end, we are shifting our focus on GPTs to Commercial and Enterprise scenarios and are stopping GPT efforts in consumer Copilot."

This makes it sound as if custom GPTs and the GPT Builder may have a future for commercial and enterprise customers, though Microsoft didn't elaborate on this possibility. Otherwise, the tool may have simply failed to catch on with subscribers, and the company might feel it's no longer worth the time and effort to support it.

Also: ChatGPT vs. Microsoft Copilot vs. Gemini: Which is the best AI chatbot?

In the meantime, what do you do if you've created custom Copilot GPTs and don't want to lose them? Here's what Microsoft advises:

Open your custom GPT in Edit mode and select the Configure tab. Copy the instructions and paste them somewhere else.

That last step seems vague since Microsoft didn't indicate how and where you could use those instructions. But at least, the data will be retained in some format. Certainly, you can save the information and then copy it back to a regular Copilot prompt when you want to run that particular request.

Artificial Intelligence

In today’s rapidly evolving world, leadership has transcended traditional boundaries. Leaders are no longer just stewards of organisational success but also torchbearers of societal change. As the world changes, companies must evolve or risk extinction, and embracing Diversity, Equity, and Inclusion (DEI) is key to this evolution.

DEI matters more than ever, especially in times of crisis when diverse teams prove to be more resilient and adaptable. They bring varied perspectives that help navigate complex challenges and drive innovation. Inclusive practices ensure that all team members feel valued and supported, boosting morale and productivity.

Equity ensures that the benefits of growth and success are shared by all, leading to a just and prosperous society.

DEI at Tesco: A Strategic Priority

For Tesco, DEI is a strategic priority because it aligns with our core values of treating people the way they want to be treated and creating a place where everybody is welcome. By fostering an inclusive workplace, Tesco harnesses the diverse perspectives and ideas of our colleagues, driving innovation and better decision-making. The CEO and senior leadership team visibly champion diversity and inclusion whilst setting bold ambitions on where we would like to be.

Mentoring and Sponsorship Programmes

Tesco has established active mentoring and sponsorship programs designed to support the professional growth and development of its colleagues. Through these initiatives, we pair colleagues with experienced mentors who provide guidance, share knowledge, and help mentees navigate their career paths. Sponsorship programs go a step further by having senior leaders advocate for their protégés, opening doors to new opportunities and facilitating career advancements.

These programs are integral to our commitment to fostering a culture of continuous learning and development. By investing in mentoring and sponsorship, Tesco not only empowers its workforce but also builds a pipeline of diverse and capable leaders for the future.

Embedding Inclusion in Everything We Do

Inclusivity starts with inclusive hiring. Inclusive job descriptions, active sourcing for diversity, and encouraging an inclusive approach to referrals are good starts. We are keen on fair employment practices, pay parity, strong anti-discrimination policies and being a disability-confident employer.

Inclusive Insurance Policies

Our comprehensive policies and initiatives show our commitment to fostering an inclusive and supportive environment. At Tesco Bengaluru, our inclusive approach begins with extensive insurance policies tailored to meet the diverse needs of our workforce. Recognising the importance of supporting colleagues through significant life changes, we proudly offer gender reassignment support, ensuring individuals undergoing transition receive the necessary care and respect that they deserve.

Acknowledging the evolving nature of family structures, our policies extend coverage to live-in partners and siblings. We are also committed to equality, providing coverage for same-gender partners and ensuring equal access to benefits for all colleagues.

Furthermore, our fertility treatment coverage supports those on the journey to parenthood. We also offer robust support for specially-abled colleagues and their family members. These inclusive policies highlight our dedication to the well-being and equality of our workforce, reinforcing our commitment to creating a supportive environment for all.

Listen, Learn, Act

Periodic feedback through employee surveys helps us measure how inclusive we are and guides us to take the right steps to drive it in a positive direction. Colleague-driven groups focussing on empowering women, ally networks and our male allies encourage everyone to bring their whole selves to work while fostering well-being and psychological safety.

Celebrating Diversity Through Events and Initiatives

At Tesco Bengaluru, we actively celebrate inclusion through dedicated events and initiatives that reflect our commitment to diversity and equality. Each year, we honour Pride Month, International Men’s Day, and International Women’s Day with a plethora of activities and discussions. These events celebrate our diverse workforce and promote understanding and respect among colleagues.

During the Inclusion Week, we engage in workshops, panel discussions, and cultural celebrations to reinforce our inclusive values. Comprehensive inclusion training for all colleagues ensures that everyone is equipped with the knowledge and skills to foster a welcoming environment.

Additionally, Disability Awareness Month allows us to focus on the contributions and needs of our specially-abled colleagues, ensuring they feel supported and valued. These celebrations and initiatives are more than just events; they are integral to our culture, underscoring our dedication to creating an inclusive and empowering workplace for everyone.

At Tesco Bengaluru, listening, learning, and acting are at the heart of our inclusive practices. Our comprehensive policies, celebratory events, and robust mentoring programs demonstrate our dedication to fostering an environment where everyone feels cherished. We believe that by embracing diversity and promoting equality, we can create a more innovative, dynamic, and successful workplace for all.

The post Transformative Leadership Strategies for Inclusive Innovation in Tech appeared first on AIM.

Chip giant Nvidia has long dominated what's known as the "training" of neural networks, the compute-intensive task of fashioning and refashioning the neural "weights," or, "parameters," of the network until they reach optimal performance. The company has always had competition, from a variety of chip makers including giants such as Intel and Advanced Micro Devices, and startups such as Graphcore.

The latest benchmark tests of speed, however, suggest Nvidia really has no competition, if competition means parties that meaningfully challenge the best the company can do.

The MLCommons, the industry consortium that compiles multiple benchmark reports each year on AI chip performance, on Wednesday announced numbers for how different chips perform when training neural nets for a variety of tasks, including training Meta's Llama large language model to make predictions, and training the Stable Diffusion image model to produce pictures.

Nvidia swept the benchmarks, getting the top score, and also the second-best, in all nine competitions. Competitors such as AMD, Intel and Google's cloud computing division, didn't even come close.

Also: Nvidia sweeps AI benchmarks, but Intel brings meaningful competition

It was the third time in a row that Nvidia had no competition for the top scores, but even the encouraging results of past rounds by competitors failed to materialize this time around.

The Training 4.0 test, which totals nine separate tasks, records the time to tune a neural network by having its settings refined in multiple experiments. It is one half of neural network performance, the other half being so-called inference, where the finished neural network makes predictions as it receives new data. Inference is covered in separate releases from MLCommons.

Most of MLPerf's tasks are by now well-established neural nets that have been in development for years, such as 3-D U-Net, a program for studying volumetric data for things such as solid tumor detection, which was introduced by Google's DeepMind back in 2016.

However, MLCommons continues to periodically update the benchmarks with new tasks to reflect emerging workloads. This round of training was the first time the submitters competed on the time to "fine-tune" a version of Meta's Llama language model, where the AI model is retrained, after its initial training, by using a more focused training data set. Also added was a "graph neural network" task, training the neural net to traverse a set of associated data points, which can be useful for things such as drug discovery.

Also: Nvidia teases Rubin GPUs and CPUs to succeed Blackwell in 2026

In the test to fine-tune Meta's Llama 2 70B, Nvidia took just a minute and a half, with a collection of 1,024 of its "H100" GPU chips, a mainstream part currently powering AI workloads across the industry. The Nvidia chips scored the top twenty-three results, with Intel's "Guadi" AI accelerator showing up in twenty-fourth place.

Even when adjusted for the number of chips, substantial challenges fail to materialize. In eight-chip configurations, for example, which is more common than a 1,024-chip system, as far as enterprises are concerned — a configuration where Intel had promising results last summer — Intel's best — and only — submission this time around was for the Llama 70B task.

It took Intel's system, aided by two of Intel's XEON CPUs, 78 minutes to fine-tune Llama. An 8-way Nvidia H100 system, aided by two of Advanced Micro Devices' EPYC processor, assembled by open-source vendor Red Hat, took less than half the time, just over 31 minutes.

In the test to train OpenAI's GPT-3 for things such as chat, Intel was able to use just 1,024 Gaudi chips, a tenth of the number of chips Nvidia used, 11,616 H100s. But Intel's score, 67 minutes, took more than twenty times as long to train as Nvidia's leading score, 3.4 minutes. Of course, some enterprises may find that a difference of an hour to train versus three minutes is negligible considering the cost savings of using far fewer chips, and given that much of the work in training AI models can be factors other than the strict wall-clock time to train, such as the time required for data prep.

Also: Intel shows off latest 'Gaudi' AI chip, pitched towards enterprises

Other vendors had an equally hard time catching Nvidia. On the venerable test of image recognition, with the neural net known as Resnet, Advanced Micro Devices took 167 minutes to train the network using six of its "RADEON RX 7900 XTX" accelerator chips, versus just 122 minutes for a six-way Nvidia "GeForce RTX 4090" system.

Google's four submissions of its "TPU" version 5 chip, all for the GPT-3 test, achieved scores far below Nvidia's, between 12 and 114 minutes to train versus Nvidia's 3.4 minutes. Past competitors such as Graphcore have since bowed out of the race.

Also conspicuous in the results are Nvidia's dominance as a system vendor. All of its winning scores were made with systems engineered by Nvidia itself, even though a raft of system vendors participated, including Dell, Fujitsu, Hewlett Packard Enterprise, Juniper Networks, and Lenovo. Of the second-place scores won by Nvidia, three were built by the company's close partner in systems design, Supermicro.

An interesting future development could be systems using Nvidia's "Grace" CPU. All of the chip results submitted, whether from Nvidia, Intel, AMD or Google, continue to use only one of two x86 CPUs, Intel's XEON or AMD's EPYC. With Nvidia aiming to sell more complete computing systems using Grace, it seems only a matter of time before the CPU gets joined with Nvidia's GPUs. That could have an interesting impact on Nvidia's already substantial lead.

An interesting first this time around for the benchmark suite was the inclusion of a measurement of the energy consumed to train neural nets. The Singapore company Firmus Technologies featured results of its cloud platform, Sustainable Metal Cloud, running tens and hundreds of Nvidia H100s, and offered the total "energy-to-train" measured in Jules.

To run the Llama 2 70B fine-tuning, for example, Firmus's cloud computing system took between 45 million and 46 million Jules to train the network using 512 H100 chips. That training run, took two minutes, a little longer than Nvidia's best time on its own. It required four times as much energy as an 8-chip system that took fifteen times as long to train, or, 29 minutes, demonstrating the remarkable increase in energy consumed with giant training systems.

The cost of training AI has been a hot-button issue in recent years, both in terms of cost burden to companies in dollar terms, and the environmental burden. It remains to be seen whether other submitters to the MLPerf results will join Firmus in offering energy measurement in the next round of benchmarks.

Samsung on Wednesday unveiled an updated roadmap for its most advanced chip nodes catered for AI chips. The South Korean tech giant also introduced a new turnkey service that leverages its multiple chip business areas to entice companies such as Nvidia and AMD to use its foundry, or contract chip production, service for their AI chips.

The announcement marks the shifting focus of Samsung Foundry, the company's contract chip-making business unit, to chips for AI and high-performance computing (HPC) rather than processors on mobile devices.

Also: Samsung's chip division wants the top spot back: What's happening?

Samsung Foundry's AI sales have increased by 80% over the past year and it was making significant strides in diversifying its customer base and application areas amid evolving market demand, the company noted. The tech giant aims to have over 50% of its foundry revenue brought in outside of mobile, it also said.

At Samsung Foundry Forum, its annual event for foundry themed this year as Empowering the AI Revolution, held at San Jose, the company showed off its new 2-nanometer (nm) and 4nm process nodes, called SF2Z and SF4U, respectively.

SF2Z incorporates what is called a backside power delivery network (BSPDN) to a conventional 2nm node (Samsung's SF2), where the power rails are placed on the backside of the wafer. Current chips have all the components in a chip such as memory, logic, and power rails on the front side of the wafer. Other contract chip makers are also preparing their own BSPDN technologies __ Intel calls it PoweVia and TSMC refers it to as Super PowerRail which they are also planning to adopt for their 2nm or under node chips.

Also: Samsung to form HVAC joint venture with Lennox to expand sales in North America

According to Samsung, putting the power rails on the backside instead enhances the power, performance, and area (PPA) and voltage drop. SF2Z is aimed at high-performance computing (HPC) and AI chips and will roll out in 2027. Samsung already said previously that SF2 will launch in 2025 prior to the forum.

Samsung was the first to start production of a 3nm gate-all-around (GAA) node in 2022. At the forum, Samsung noted that its GAA process was maturing in terms of performance and yield and a second-generation 3nm node called SF3 will launch later this year. GAA will also be adopted to its 2nm node launching next year, the company said.

SF4U, meanwhile, is a variant of its 4nm, SF4 process that offers PPA boosts using optical shrink, where an existing die design by the customer is scaled down to fit into the newer node. This saves them in cost as no major architectural changes in the chip's design is required for them to migrate into the more advanced node. SF4U is launching in 2025 while Samsung already offers SF4 to customers. The South Korean tech giant also reiterated that its 1.4nm node (SF1.4) will launch in 2027 and was also preparing for chips below 1.4nm.

Samsung also unveiled its new turnkey foundry platform dubbed Samsung AI Solutions. This platform is offered together by Samsung's three business units in its chip division, Foundry, Memory, and Advanced Package (AVP).

Also: Samsung Galaxy Ring: Features, price, availability, and everything else we know

According to the company, the platform integrates the "unique strengths" of each of these business units that will allow Samsung to offer solutions for customers tailored for the specific requirements for their AI chips. Overall, it will provide more bandwidth in a compact form factor, reduce power consumption, and improve signal integrity, Samsung said.

Because it is the only chipmaker that can offer memory chips to go with the customer's chip, manufacture these chips as well as package them, Samsung said this streamlines supply chain management and reduces time to market for the customer. The company said Samsung AI Solutions offers a 20% improvement in total turnaround time for customers. Samsung AI Solutions will be offered with co-packaged optics (CPO) __ where even the optics are packaged __ in 2027, the South Korean tech giant said.

Samsung's decision to add more variants to its process nodes makes sense as a strategy for AI, a general term that in fact encompasses a wide variety of different chips designed for different tasks and scales. Some are made for ChatGPT and other large language models. Some are for vision and image processing for use in drones and display devices. Others are AI accelerators for data centers.

According to market research firm Omdia, the global foundry market is expected to grow 18.1% a year on average from 2023 when it was worth $103.55 billion to 2027, when it is expected to be worth $201.28 billion. Growth in cutting-edge nodes of 3nm and under will have the most marked growth at 92.3% per year on average over the time period, the research firm's forecast noted.

Samsung is the world's largest memory chip producer and the second-largest contract chipmaker. Its Memory Business is attempting to win Nvidia as a customer for its HBM3E chips, which are used together with the GPU maker's AI accelerators.

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