Salesforce, a global cloud solutions provider, has announced Genie at the Dreamforce customer conference, a real-time data integration platform using which enterprises can deliver seamless services across sales, marketing, and commerce.
David Schmaier, Chief Product Officer and President at Salesforce, said that the company had built Genie to automate every service provision by Customer 360, Salesforce’s customer relationship management (CRM) platform. Salesforce Genie forms the core of real-time Customer 360, collects, stores, and integrates real-time data streams with Salesforce transactional data.
Genie underlines the Salesforce platform by smoothing data movement whenever required. Patrick Stokes, GM and EVP of Salesforce, said, “So we’re announcing that our Customer 360 applications now have access to an entirely new way of bringing data into Salesforce in real time at scale that we’ve never been able to achieve before.”
Stokes highlighted that Genie is a lakehouse architecture and a modern equivalent of the company’s previous attempts to integrate transactional data in the CRM database. However, Genie is more than just an integration layer added to the platform.
Genie offers Sales Cloud, Service Cloud, Marketing Cloud, and Commerce Cloud services separately. It also features services for Tableau, MuleSoft, and Slack. A part of its ability to offer such capabilities is developed on Salesforce’s cloud infrastructure, Hyperforce, which offers data security, privacy, and regulatory compliance controls. This ensures customers’ trust and reliance on the platform. You can check the entire list of services here.
NVIDIA releases Maxine, a suite of GPU-driven software development kits (SDKs) to deliver breakthrough audio and video quality. Maxine enables clear communications via its cloud-native microservices for augmented-reality effects and audio-video enhancement.
With the early-access release of Maxine’s audio effects, the company said that Maxine would be re-architected for cloud-native microservices. Additionally, new SDK capabilities, including Speaker Focus and Face Expression Estimation, were announced, along with the availability of Eye Contact to all users. Updated versions of existing SDK functionalities are also included in NVIDIA Maxine.
Maxine provides three updated GPU-accelerated SDKs for audio, video, and AR effects that revolutionize real-time communications with AI. A new feature called Speaker Focus isolates the audio tracks of foreground and background speakers to make each voice more audible. Lastly, the Audio Super Resolution SDK function has also received an upgrade with better quality.
The video effects SDK uses a regular webcam to produce AI-based video effects. Enhancements to temporal stability have been made to the Virtual Background function, which divides a person’s profile into sections and uses AI-powered background removal, replacement, or blur.
Additionally, the AR SDK offers typical web camera feed-based, real-time 3D face tracking and body pose estimation driven by AI.
Other cloud-native microservices offered by Maxine will enable developers to create real-time AI applications. These services may be autonomously managed and deployed on the cloud, speeding up implementation time. Some of these microservices are:
Background Noise Removal
Room Echo Removal
Audio Super Resolution
Acoustic Echo Cancellation
Maxine is a part of the NVIDIA Omniverse Avatar Cloud Engine, a set of cloud-based AI models and services that developers may use to create, personalize, and use interactive avatars. You can refer to the GTC keynote for more information.
During the GTC event, NVIDIA announced its new DGX system software and infrastructure to power innovation in enterprise AI development. The company announced that NVIDIA DGX H100 systems are now available for order. Based on the latest GPU chips, these systems will form the building blocks for NVIDIA’s full-stack AI solutions.
The company launched the new NVIDIA Base Command software to simplify and accelerate AI developments by powering the DGX systems. The software will enable enterprises to tap the potential of their investment in NVIDIA’s DGX systems for orchestration and network infrastructure.
NVIDIA unveiled the DGX BasePOD to make AI deployments simpler and faster. The BasePOD provides an architectural framework for all DGX computing, storage, network, and software systems.
The company has also created an advanced version of the BasePOD, the NVIDIA DGX SuperPOD. The DGX SuperPOD is a comprehensive hardware, software, and services package that removes the guesswork from developing and deploying AI infrastructure in any enterprise, making it the fastest route to AI innovation.
The GTC event also unveiled the NVIDIA Partner Network, a network of fully integrated and readily deployable offerings provided to valued partners. The program is intended for business models, including value-added reselling, solutions integration, system design or manufacture, hosting services, consultancy, or NVIDIA products and solutions.
NVIDIA is driving more and more architectural decisions and modifications in its CPU and GPU accelerator engines with each new generation. Jensen Huang, CEO of NVIDIA, announced that the company would ramp up Hopper, an architecture supporting AI workloads. The Hopper architecture is intended to scale diverse workloads for data centers.
NVIDIA unveiled Hopper in March, along with other advancements like the NVIDIA Grace CPU. This month, the company released benchmark results for the chip in the MLPerf suite of machine learning tasks.
Hopper is built with approximately 80 billion transistors with NVIDIA’s cutting-edge TSMC 4N technology and features multiple innovations to enhance the performance of NVIDIA H100 Tensor Core GPUs.
The company has pushed the H100 Tensor Core GPUs to enter the production zone in total volume. The GPU chips will be shipped to companies including Hewlett Packard, Dell, Cisco Systems, etc. NVIDIA systems with the H100 GPU will enter the market in the first quarter of next year.
When the company launched the first H100 GPU chip, Huang said, the chips would be “the next generation of accelerated computing.” The H100 chip is designed to accomplish artificial intelligence tasks for data centers. The company claims that H100 chips “dramatically” reduce deployment costs for AI-based programs. For instance, the performance of 320 top-of-the-line A100 GPUs is equivalent to only 64 H100s.
Nvidia has announced Nvidia Omniverse Cloud, its first software and infrastructure-as-a-service offering, at Nvidia GTC 2022. It is a suite of cloud services for artists, developers, and enterprise teams to design, publish, operate, and experience metaverse applications anywhere.
The technology uses the cloud to tap the heavy-duty power of data centers to enable Omniverse tools wherever the users happen to be. More than 700 companies and 200,000 people are using Omniverse now.
Using Omniverse Cloud, individuals and teams can experience in one click the ability to design and collaborate on 3D workflows without the need for any local computing power. Omniverse Cloud will leverage Nvidia’s cloud gaming solution, GeForce Now, which has a global graphics delivery network.
“The next evolution of the internet called the metaverse will be extended with 3D,” said Richard Kerris, vice president of the Omniverse at Nvidia. “To understand what the impact of that will be, the traditional internet that we know today connects websites described in HTML and viewed through a browser. The metaverse will be the evolution of that internet connecting virtual 3D worlds using USD, or universal scene description.”
Omniverse Cloud is based on the open Universal Scene Description (USD) standard for interoperable 3D assets.
“The metaverse, the 3D internet, connects virtual 3D worlds described in USD and viewed through a simulation engine,” said Jensen Huang, Nvidia CEO, in a statement. “With Omniverse in the cloud, we can connect teams worldwide to design, build, and operate virtual worlds and digital twins.”
Nvidia has announced NeMo LLM, its first cloud service to make AI less complicated. NeMo LLM will focus on making large language models more accessible for experimentation and deployment across multiple domains.
Ian Buck, GM and VP of Accelerated Computing at Nvidia, said that many AI models need to be turned into more accessible applications so that enterprises can fit them in real-world settings. NeMo LLM adds a layer of intelligence and interactivity to enable user interaction with complex AI models like DALL-E 2. Such language models are trained on billions of parameters, making model tuning a challenging task.
Nvidia’s NeMo LLM service will add a conversational element to many such models across domains like finance, medicine, or technology. Buck said, “This service will help bring large language models to all sorts of different use cases – to generate profit summaries, for product reviews, to build technical Q&A, for medical use cases.”
NeMo LLM takes some pre-trained models like NeMo Megatron (trained on 530 billion parameters), GPT-3 (trained on 175 billion parameters), or T5 (trained on 11 billion parameters); and constructs a domain-based framework around it. This saves the need to train a model from scratch.
Nvidia is also launching the BioNeMo service along with NeMo LLM to provide researchers with access to pre-trained biology and chemistry language models. It is aimed to aid researchers in interacting with and manipulating protein and data for drug discovery. The initial two BioNeMo protein models, ESM-1 and ESM-2, cater to encoding essential biological information of large protein databases and predicting 3D protein structures from amino acid sequences, respectively.
The NeMo LLM cloud service will be the recent addition to Nvidia’s stable software machines, like RIVA and Merlin.
The United Nations Educational, Scientific and Cultural Organization, or UNESCO, has inaugurated the State of the Education Report for India: Artificial Intelligence in Education. This is the fourth edition of the annual flagship report of the New Delhi UNESCO office.
Based on extensive research and study, the report provides insight into the state of artificial intelligence and its market in the country. It talks about AI as a subject and its application in the education sector. As per the report, the Indian AI market will reach a net worth of US $7.8 billion by 2025, showcasing a compound annual growth of 20.2 percent!
The press release mentions 10 recommendations by UNESCO for promoting AI in education. These recommendations specify AI ethics as a priority, the need for a regulatory framework, effective public-private partnerships, expanding AI literacy, work on correcting algorithmic biases, and a few others.
Eric Falt, Director at UNESCO, New Delhi, said, “India has made significant strides in its education system, and strong indicators point to the country’s notable efforts to enhance learning outcomes, including by using Artificial Intelligence-powered education technology.”
He also mentioned that artificial intelligence is one of the areas where the Indian government has advanced and made tremendous strides in the last few years.
Stanford University and researchers at the Harvard Medical School have developed an artificial intelligence model that detects abnormalities and diseases by studying NLP-based reports. The AI model does not rely on standard human annotations of X-rays to learn to predict diseases.
Using AI in medical imaging technologies is not a new advancement. However, many challenges still limit its application to only a handful of clinical applications. A massive amount of data and human annotations must go into training the standard disease prediction models.
However, the model created by Harvard and Stanford, called CheXzero, has shown accurate results by relying on reports created by NLP rather than human annotations. The model is self-supervised, meaning that it can train itself to learn more. Self-supervised algorithms automatically address the issue of over-dependence on labeled data.
Pranav Rajpurkar, assistant professor at HMS, said, “Up until now, most AI models have relied on manual annotation of huge amounts of data—to the tune of 100,000 images—to achieve a high performance. Our method needs no such disease-specific annotations.”
Researchers have used chest X-rays as an example to show CheXzero’s capabilities, but it can be generalized to a vast array of other medical setups that deal with unstructured data. The AI model helps bypass the requirement of large-scale labeling bottlenecks that have been a long-standing challenge in medical machine learning.
Adept, an AI and ML product company, announced a large-scale transformer ACT-1, an AI assistant that can browse, search, and use the web like humans. When provided with instructions, the AI model behaves like a personal assistant in software and navigates the web, scrolls, likes, and types whenever required. The company has released a demo video of how ACT-1 works.
https://t.co/2lGCWzPHfc has unveiled ACT-1, a large Transformer trained to use digital tools such as a web browser. It’s hooked up to a Chrome extension that allows ACT-1 to observe the browser & take actions, like clicking, typing, & scrolling, etc: https://t.co/V6IrhuG8SHpic.twitter.com/eRIWUIbfL3
ACT-1 has been developed to work with digital tools, and has recently learned to use a web browser. It connects to a chrome extension that allows it to observe users’ actions in the browser and performs activities like searching and scrolling. The action space includes UI elements on the page, and the observation is rendered across other websites universally.
Process high-level user requests/queries with only a command text. In this case, getting a single task done necessitates conducting activities and noting observations frequently.
Work with spreadsheets and exhibit real-world knowledge in inferring context and assisting computations.
Combine multiple tools to finish a complex task.
The large-scale transformer ACT-1 is still in its infancy and will become more useful in the future as it is continually seeking advanced training and enhancements. It is incredibly coachable and can fix errors with just one human feedback. However, there is a potential risk for ACT-1 being misappropriated with hateful input commands.
Adept plans to work on preventing any possible misuse by utilizing machine learning techniques and carefully staging deployment.
A country’s ability to develop its technology is one of its greatest assets, and adding AI applications to supercomputers is just the beginning. Supercomputers are high-performance systems where computational power is measured in floating-point operations per second (flops). The inventions of supercomputers date back to 1964, and the age of supercomputers in India started in the 1980s. In November 1987, the Indian Government decided to create C-DAC, the center for Development of Advanced Computing technology. C-DAC started the PARAM supercomputers, led by Vijay P. Bhatkar, the architect of India’s national initiative in supercomputing since the 1990s. Today, supercomputers in India are among the fastest 500 in the world. One of those supercomputers is a series of PARAM. PARAM means ‘supreme’ in Sanskrit, devoting the idea of supreme computer systems. Here is the list of PARAM supercomputers, organized by the year of their launch.
1. PARAM 8000
PARAM 8000 is the first machine in the PARAM supercomputers series built from scratch in 1991. A prototype of the PARAM 8000 supercomputer came second in the 1990 Zurich supercomputing show, where it was introduced and tested. PARAM 8000 was launched in the market in August 1991 with a 64-node machine, making it India’s first supercomputer. It was a collaboration of C-DAC and the Institute of computer aided design (ICAD), Moscow. PARAM 8000 was successful with its Inmos T800/T805 transputers, distributed memory MIMD (Multiple Instruction, Multiple Data) architectures, and a reconfigurable interconnection network. First installed in ICAD, Moscow, it rapidly took over the home market, attracted 14 other buyers, and was later exported to Germany, the UK, and Russia.
2. PARAM 8600
In 1992, PARAM 8600 was designed in the light of C-DAC wanting to make India’s supercomputer more powerful by integrating the Intel i860 processor. PARAM 8600 supercomputer is an upgrade to PARAM 8000, where the node structure was changed from Inmos T800/805 to one i860 and four Inmos T800 transputers. Each PARAM 8600 cluster resulted in as powerful as four times the PARAM 8000 cluster.
3. PARAM 9000
PARAM 9000 was developed in 1994 to merge cluster processing and massively parallel processing computing workloads. The standard PARAM 9000/SS used SuperSPACRC II processor variant, PARAM 9000/US used UltraSPARC processor, and PARAM 9000/AA used DEC Alpha. To accommodate newer processors, the design of PARAM supercomputers changed to modular with this version by scaling up to 200 CPUs with 32-40 processors and using Clos network topology.
PARAM 10000 was launched in 1998 based on SMPs (symmetric multiprocessors) clusters that is a relevantly replicated UNIX OS. It contains independent nodes where each node is based on the Sun enterprise 250 server with two 400 Mhz UltraSPARC II processors. PARAM 10000 was exported to Russia and Singapore with the base system’s best speed recorded at 6.4 GFLOPS (giga-floating point operations per second). The base configuration got three compute nodes and a server node, and the system has 160 CPUs capable of 100 GFLOPS. However, it is easily scalable to the TFLOP (trillion floating point operations) range.
5. PARAM Padma
PARAM Padma is a 1Teraflop supercomputer, which is India’s first supercomputer to earn a place, ranked 171th in the Top500 list of supercomputers of the world in June 2003. This PARAM supercomputer was launched in 2002 with a storage capacity of 1TB, 248 IBM Power4 1GHz processors, IBM AIX 5.1L Unix OS, and PARAMNet for the main connection.
6. PARAM Yuva
PARAM Yuda came out in November 2008 with a peak speed (Rmax) of 38.1 Tflops and a maximum speed (Rpeak) of 54 Tflops. It has a storage capacity of 25TB up to 200TB, 4608 cores, Intel 73XX-2.9 GHz processor, and PARAMNet 3 as the primary connection. PARAM Yuva ranked 69 in the Top500 list of supercomputers in the world after PARAM Padma as India’s supercomputer.
7. PARAM Yuva II
PARAM Yuva II was developed in February 2013, the project took three months and cost ₹160 million. The investment paid off as PARAM Yuva II became the first India’s supercomputer to reach 500 Tflops. PARAM Yuva II is a high-performance computing cluster that uses 35% less energy compared to other PARAM supercomputers and performs ten times quicker at 524 Tflops. It has a hybrid cluster with multiple interconnects, a high storage capacity of 200TB, and supports software for parallel computing. In the series of PARAM supercomputers, Yuva II is an upgrade of PARAM Yuva, which was created for the purpose of a research-oriented computational environment. PARAM Yuva II is a milestone for C-DAC in PARAM supercomputers as it ranked 1st in India, 9th in the Asia Pacific region, and 44th worldwide among the list of most powerful computer systems. Additionally, PARAM Yuva II earned a position on the Green500 list in November 2013 and again in June 2015, and also it ranked 172 in the Top500 supercomputers list in June 2015.
8. PARAM ISHAN
Param ISHAN was developed and launched in September 2016 at the Indian Institute of Technology Guwahati. It has a hybrid high-performance computing system with a peak computing performance of 250 Tflops. PARAM ISHAN has 162 computer nodes, including 126 nodes having 2 Intel Xeon E5-2680 v3, 12 cores, 2.5 GHz processors, and 64 GB RAM per node. Also, four high memory compute nodes, 16 nodes containing 2 NVIDIA Tesla k40 (GPGPU) per node, and the rest 16 nodes having 2 Intel Xeon Phi 7120 (MIC) per node. PARAM ISHAN is first India’s supercomputer with a 300TB storage capacity based on a luster parallel file system and a software stack comprising CentOS 6.6, Intel parallel studio 2016, GNU compilers, Intel MPSS, CUDA, Mellanox OFED, Luster, SLURM resource manager & scheduler and Bright cluster manager.
9. PARAM Brahma
PARAM Brahma was built in India by C-DAC and IISc under the national supercomputing mission (NSM), co-funded by the ministry of electronics and information technology and the department of science and technology. It has a computational power of 797 Tflops (Rpeak) and 526.5 Tflops (Rmax) with a storage capacity of 1PB. The unique property of PARAM Brahma is that it has a cooling system called direct contact liquid. This cooling system uses the thermal conductivity of liquids, mainly water, to maintain the system’s temperature during operations. PARAM Brahma supercomputer was launched in 2019 and, as of 2020, is available at IISER Pune and has 2 X Intel Xeon Cascadelake 8268, 24 cores, and 2.9 GHz processors.
PARAM Siddhi-AI is the fastest among the PARAM supercomputers, with a Rpeak of 5.267 Pflops and a sustained Rmax of 4.6 Pflops. It is a high-performance computing artificial intelligence (HPC-AI) system built in India. The integration of AI in supercomputers helps research for advanced materials, computational chemistry and astrophysics, health care system, flood forecasting, faster simulations in the covid-19 application, and medical imaging and genome sequencing. PARAM Siddhi-AI was released in 2020, containing the NVIDIA DGX SuperPOD based networking architecture, HPC-AI engine software and frameworks, and cloud platform. It ranked 63rd in the Top500 list of supercomputers worldwide in November 2020 and is one of top India’s supercomputers sharing the position with the Pratyush supercomputer.
11. PARAM Pravega
PARAM Pravega is a recently released supercomputer in January 2022 under the national supercomputer mission at the Indian Institute of Science, Bengaluru. It hosts an array of program development tools, utilities, and libraries for developing and executing high-performance computing operations. PARAM Pravega runs on CentOS 7.x, has a combination of heterogeneous nodes, including Intel Xeon Cascadelake processors for CPU nodes and NVIDIA Tesla V100 cards for GPU nodes, and has a storage capacity of 4PB. The peak computing power of PARAM Pravega is 3.3 Pflops.
12. PARAM Ganga
Under the national supercomputing mission, PARAM Ganga is established at the Indian Institute of Technology Roorkee. Among PARAM supercomputers, PARAM Ganga is based on heterogeneous and hybrid configurations of nodes similar to PARAM Pravega. It has 312 nodes, combining CPU, GPU, and HM modes with a peak computing power of 1.67 Pflops. The cluster of the supercomputer contains compute nodes connected to Mellanox (HDR) InfiniBand interconnect network. In addition, the PARAM Ganga supercomputer uses a luster parallel file system and runs on CentOS 7.x.
13. PARAM Shakti
PARAM Shakti is a petascale supercomputer in the PARAM supercomputers series built at the Indian Institute of Technology Kharagpur under the NSM launched in March 2022. This supercomputer facility aims to amplify the research and development initiatives in academics and industries in India and focuses on solving large-scale problems in various fields of science and engineering. PARAM Shakti has 17680 CPU cores and 44GPUs and an RHDX-based cooling system, with a computing power of 1.6 Pflops.
