Online learning platform Udacity has partnered with Infosys to launch a new free nanodegree Tech Scholars program.
The scholarship program is aimed at imparting valuable and relevant skills to learners to help them grow their careers in fields like Java programming and data operations analysis.
According to Udacity, the Infosys-Udacity Tech Scholars Program aims to assist students who do not yet hold a four-year college degree in learning valuable concepts.
Students from multiple localities in the United States, including Tempe, Richardson, Raleigh, Jersey City, Atlanta, Chicago, Seattle, and others, can apply for this scholarship program.
This scholarship program will provide students with Hundreds of hours of free training modules and tutorials on Java programming and data analysis, along with their real-world applications. Moreover, learners can also access 1:1 mentoring from these advisors.
Udacity will start this course on 25th May 2022 and run till 15th September 2022. However, as it is a self-paced course, learners can decide the timeframe within which they choose to finish the program.
Candidates must be above 18 years old with a high school diploma or other equivalent qualification and have the right to work in the US without sponsorship are eligible to apply for this scholarship program offered by Infosys and Udacity.
The application process has already begun, and applicants must submit their online application forms before 8th May 2022. Interested candidates can apply for this scholarship from the official website of Udacity.
The European Union (EU) accuses Apple of restricting its rivals’ access to iPhone’s payment technology.
The allegation put on Apple is regarding the company’s act of blocking competitors from its popular “tap-as-you-go” iPhone payment system.
According to early findings, the world’s most valuable company Apple may have infringed competition law by preventing competitors from utilizing its technology. If the charges are sustained, the company may face penalties of up to 10% of its global turnover, which was $36.6 billion last year.
“Apple has unfairly shielded its Apple Pay wallets from the competition. If proven, this behavior would amount to an abuse of a dominant position, which is illegal under our rules,” said Margrethe Vestager, the EU’s antitrust chief.
Vestager further added that the preliminary conclusion that they reached relates to mobile payments in shops by excluding others from the game.
According to the Commission, this behavior by Apple has an “exclusionary effect” on competitors and “leads to less innovation and fewer choices for consumers for mobile wallets on iPhones.”
In its defense, an Apple spokesperson said, “Apple Pay is only one of many options available to European consumers for making payments and has ensured equal access to NFC while setting industry-leading standards for privacy and security.”
The spokesperson also mentioned that they would continue to work with the Commission to ensure that European customers have access to their preferred payment method in a safe and secure environment. Apple is also concerned that tweaking its payment system may make transactions less secure.
Integrated security solutions providing company Guardforce AI announces the successful deployment of its over 4800 robots at multiple locations across the globe.
The company said that the locations include nine countries worldwide, such as Hong Kong, Macau, Thailand, Malaysia, Singapore, Dubai, the USA, Guangzhou, and Shenzhen.
Government buildings, hospitals, hotels, tourist sites, restaurants, sports centers, supermarkets, and transportation facilities are among the sectors where Guardforce AI robots have been deployed.
Moreover, Guardforce AI is expecting to pump the number of robots to 10000 by the end of this year.
Chairman of Guardforce AI, Terence Yap, said, “In just February of this year, we announced we had surpassed 1,400 robot deployments. I am pleased to announce we have now surpassed 4,800 robot deployments worldwide, achieving an important milestone in our global expansion strategy.”
He further added that their Robotics-as-a-Service (RaaS) business model constantly changes in response to customer input. Customers are offered early pilot programs as part of Guardforce AI’s rollout strategy, which allows the company to gather feedback and upsell new features and applications.
According to the company, Guardforce AI will be able to quickly establish a substantial presence in the booming robotics sector. Terence Yap founded Guardforce AI, which is an integrated security solutions provider trusted to secure and move high-value items for both the public and commercial sectors.
The solutions offered by the company’s robots include features such as disinfection, hotel check-in/check-out services (under testing), patrol, temperature screening, and advertising. Guardforce AI successfully tested ad placements on its robots in Macau, where clients could purchase ad space. While in Hong Kong, it is now experimenting with hotel self-service check-in and check-out robot integration.
“We are also further developing our Intelligent Cloud Platform (ICP) to integrate AI solutions with our robots that we believe will create value by enhancing the customer experience while improving operational efficiency.,” added Yap.
Meta, the parent company of Facebook, Instagram, and WhatsApp, has unveiled a new AI research initiative aimed at better understanding how the human brain processes language. Meta AI is examining how AI language models and the brain respond to the identical spoken or written words in partnership with Neurospin (CEA) and INRIA.
Meta AI has spent the last two years analyzing how the brain interprets words and phrases using deep learning algorithms applied to public neuroimaging data sets. Several academic organizations, including the Max Planck Institute for Psycholinguistics and Princeton University, gathered and shared the data sets. Each institution acquired and shared the data sets with the volunteers’ informed consent in compliance with the legal regulations established by their respective ethics committees, including the authorization sought from the study participants.
Language Model is an artificial intelligence (AI) model that has been trained to anticipate the next word or groups of words based on the preceding words or phrases. It is part of the technology that predicts the next word you want to type on your phone, allowing you to finish your message sooner. The early versions of language models used classic statistical approaches such as N-grams, Hidden Markov Models (HMM), and followed rule-based linguistic criteria to learn the probability distribution of words. In contrast, language models of today are created using neural networks that have been trained on vast amounts of textual data. These AI models that most closely resemble the human brain to anticipate the next word using natural language processing (NLP).
Despite having profound scope, these models fall short of performing at par with the human brain. For instance, while a human baby can simultaneously parse that ‘orange’ can refer to both a fruit and a color, machines fail to draw such a correlation. Hence researchers at Meta are on a mission to understand the working of the human brain that can help them develop better language models. The believes that insights from this study can offer enough ideas and guidance in the pursuit of developing AI that processes speech and text as efficiently as people.
Speaking language, according to Jean-Rémi King, a senior research scientist at Meta AI, makes humans completely unique, and knowing how the brain works is still a problem and a work in progress. The fundamental question, according to King, is “What makes humans so much more powerful or so much more efficient than these machines? We want to identify not just the similarities, but pinpoint the remaining differences.”
The researchers modeled hundreds of brain scans while also using magnetoencephalography (MEG) scanner to take pictures every millisecond, using public neuroimaging datasets extracted from images of brain activity in magnetic resonance imaging (MRI) and computed tomography (CT) scans of participants. Working with INRIA, they compared a number of language models to the brain reactions of 345 volunteers while they listened to complicated narratives using functional magnetic resonance imaging (fMRI).
Next, AI systems were given the same narratives that were read or presented to human beings. The researchers then examined the two sets of data to identify where they overlapped and where they didn’t. Meta researchers inferred from their findings that language models that closely reflect brain activity are the best at predicting the next word from context like “on a dark and stormy night…” or “once upon a time…”. Self-supervised learning (SSL) in AI is focused on prediction based on partially visible inputs, and it might be crucial to how individuals acquire the language.
The results of Meta AI revealed that particular brain areas, such as the prefrontal and parietal cortices – situated in the front and middle of the brain, respectively – better-represented language models with far-off future word predictions. In other words, specific parts of the brain anticipate words and concepts months quite in advance, whereas most language models today are trained to predict the next word. Unlocking this long-term predicting power might aid in the advancement of contemporary AI and language models.
The researchers also discovered that the human brain can learn with just a few million phrases and can adapt and retain knowledge in its trillions of synapses on a constant basis. Conversely, AI language models can parameterize up to 175 billion artificial synapses after being trained on billions of phrases.
Meta AI researchers and NeuroSpin are presently working on an original neuroimaging dataset to refine their research. This will be open-sourced, along with code, deep learning models, and academic papers, to facilitate further AI and neuroscience research. The intent, according to King, is to create a set of tools that other peers in academia and other fields can utilize and benefit from.
He added that researchers can significantly enhance contemporary AI language models by researching long-term predicting capacity in more depth. The META team believes that by incorporating long-term forecasts into algorithms, they can become more parallel to the brain.
Technology giant Amazon announces its plans to create 2500 additional corporate and tech jobs in California for its effort to expand in Santa Monica, Irvine, and San Diego.
Amazon has signed a lease for a 200,000-square-foot space with J.P. Morgan Asset Management at the Water Garden, managed by CBRE, to house the new employees over the coming years.
As part of its expansion strategy, the company is looking to hire local talent and continue developing and innovating on behalf of its clients.
Amazon is hiring for positions in software development, engineering, game design, user experience, and other areas across Amazon teams such as retail, games, operations, and Amazon Web Services.
This new expansion comes after Amazon launched more than 15 locations around Southern California in 2021, resulting in the creation of over 17,000 employees. Candidates from California can now take advantage of Amazon Returnship, a program designed to assist professionals who have lost or quit their jobs, including those displaced due to the COVID-19 pandemic.
Holly Sullivan, Vice President of Worldwide Economic Development at Amazon, said, “Communities across California have welcomed Amazon over the past decade and have seen firsthand how our investments can unlock new opportunities for their neighbors and local economies. We’ve created more than 170,000 jobs across the state and are committed to continue investing here.”
Sullivan further added that building cloud infrastructure, refining the Alexa experience, and inventing cutting-edge video games are among the 2,500 new opportunities. The company claims that these jobs offer an incredible opportunity for Californians from all walks of life to join Amazon and create a great career.
Moreover, people interested in working in technology can also use Amazon’s newest recruiting initiative, Best Fit. This unique program allows software engineers to apply once and be considered for multiple jobs across several Amazon teams.
End-to-end cloud app security solution developing startup Traceable raises $60 million in its recently held series B funding round led by Institutional Venture Partners (IVP) and Tiger Global.
Other investors like Unusual Ventures & BIG Labs also participated in the company’s latest funding round. This funding has now increased the startup’s market valuation to over $450 million.
According to the company, it plans to use the freshly raised capital to invest more in product development and research, increase its sales and marketing teams, and expand global sales.
“API security has become a major security and compliance concern for most companies. Traceable offers a fundamentally differentiated solution that provides coverage across the full DevSecOps software lifecycle – from API development and testing to runtime protection,” said Steve Harrick, General Partner, IVP.
He further added that Jyoti Bansal and Sanjay Nagaraj, the company’s founders, are seasoned entrepreneurs with whom they have had the pleasure of working in the past as they pay attention to client needs and know how to produce software at scale.
United States-based technology company Traceable was founded by Jyoti Bansal and Sanjay Nagaraj in 2018. Traceable specializes in providing end-to-end distributed tracing, cloud-native integrations, and advanced behavioral analytics to provide modern application and API security.
The company’s one-of-a-kind platform called Traceable AI is the first API security platform that identifies, maintains, and secures APIs for enterprise-level businesses.
Traceable uses machine learning technology to analyze data from the user and session to the application code to learn normal application behavior and detect activities that deviate from it. To date, the startup has raised more than $79 million from multiple investors over two funding rounds.
“Traceable AI applies the power of machine learning and distributed tracing to truly understand how an application really works in the context of the business, and therefore, be able to detect anomalies and block threats to keep customers secure and resilient against next-gen attacks,” said Jyoti Bansal.
BML Munjal University (BMU) announces the launch of its new BSc Computer Science (CS) course with a specialization in cyber security, big data analytics, data science, and artificial intelligence (AI).
BMU’s new three-year-long course will begin the first batch from the academic session 2022-2023.
According to the university, the course curriculum will adhere to the National Education Policy 2020 (NEP), the National Skills Qualifications Framework (NSQF), and the University Grants Commission’s choice-based credit systems and result-based education.
The School of Engineering and Technology at BMU will offer this BSc degree program to students.
Anirban Chakraborti, Dean, School of Engineering Technology, said, “Understanding the talent gap in Data Science and AI in India and in response to the recent demands in the industry 4.0, we at BMU have come up with the course.”
He further added that this course is founded on the notion of interdisciplinary immersion experience learning, with an emphasis on systems thinking and a choice-based curriculum with optional electives.
The global market is booming and needs new talents in cutting-edge technologies like data science and AI. However, not all institutes are offering courses that cater to this need. BMU’s new BSc course will impart critical knowledge and skills that learners require to kick start their careers in the sector of new-age technologies.
Candidates must have a Higher Secondary Certificate (10+2) in the Science stream with an aggregate score of 60 percent and Mathematics as one of the compulsory subjects to be eligible for admission. Students who have not taken any conventional examinations will be required to take the BMU-SAT, a BMI-administered standardized aptitude test.
Interested candidates can learn more and apply for the course from the official website of BMU.
Meta, formerly known as Facebook, launched Large Language Models to help artificial intelligence (AI) researchers.
With this new development, researchers can now easily access OPT-175B, a vast language model with 175 billion parameters trained on publicly available data sets.
Large language models, according to Meta, are natural language processing (NLP) systems with hundreds of billion parameters that have revolutionized NLP and AI research in recent years.
The language models demonstrate an incredible new ability to write creative content, perform simple math problems, answer reading comprehension tests, and more after being trained on a large and varied text volume.
Meta is making OPT-175B, and the codebase used to train and deploy the model available to the public utilizing only 16 NVIDIA V100 data center GPUs. Full research access to large language models is frequently limited to a “few well-resourced labs,” says the company.
Meta mentioned in a blog, “We believe the entire AI community-academic researchers, civil society, policymakers, and industry — must work together to develop clear guidelines around responsible AI in general and responsible large language models in particular, given their centrality in many downstream language applications.”
The blog further stated that Meta thinks that releasing OPT-175B and smaller-scale baselines will broaden the range of voices defining the ethical implications of such technologies.
Moreover, Meta designed OPT-175B with energy efficiency in mind, training a model of this scale with only 1/7th the carbon footprint of GPT-3 and also released a set of smaller-scale baseline models that were trained on the same data set and used similar settings as OPT-175B, allowing researchers to investigate the influence of size on its own.
Interested individuals can send requests to access OPT-175B through a request form.
World’s one of the most popular social music platforms, SoundCloud acquires cutting-edge AI music company Musiio. Neither company provided any information regarding the valuation or terms of this recently signed acquisition deal.
The acquisition of Musiio will allow SoundCloud to expand its music intelligence capabilities that will help the company better harness its vast data to identify what’s next in music trends and talent.
Musiio will now play a vital role in SoundCloud’s discovery experience and help to identify talent and trends ahead of anyone else.
President of SoundCloud, Eliah Seton, said, “SoundCloud hosts more music from more creators than any platform on the planet. Acquiring Musiio accelerates our strategy to better understand how that music is moving in a proprietary way, which is critical to our success.”
Seton further added that they are thrilled to be working with Hazel and Aron (of Musiio) and to welcome Musiio’s incredible team of innovators to the SoundCloud family.
Singapore-based artificial intelligence company for the Music Industry, Musiio, was founded by Aron Pettersson and Hazel Savage in 2018. Musiio specializes in providing a technology that focuses on B2B audio reference search, automatic tagging, and playlisting capabilities for the music industry.
Musiio claims to be able to manage music using AI in a cost-effective, quick, and scalable manner, either through APIs or custom dashboard solutions. To date, the firm has raised $1 million from investors like Wavemaker Partners and Entrepreneur First over two funding rounds.
“SoundCloud is not only a legendary household name but also an artist forward business that I believe is the future of the new, holistic music industry,” said CEO of Musiio, Hazel Savage. Hazel also mentioned that she is delighted that Musiio has now joined SoundCloud after four years of establishment.
Scientists at the University of Texas, Austin, announced that they engineered Hydrolase, a plastic degrading enzyme with help of AI. It’s an enzyme that can effectively break down plastics such as polyethylene terephthalate (PET). This enzyme can successfully break down PET plastic in under 24 hours.
Hal Alper, a lead researcher in the McKetta Department of Chemical Engineering at UT Austin, said that this new enzyme brings various recycling possibilities. And not just in the waste management industry, but in all sectors to improve their plastic recycling. Han also said that we could begin to describe a true circular plastics economy with a more sustainable enzyme.
PET accounts for 12% of man-made solid waste and is one of the most common plastics found in products like disposable food trays and soda bottles. Previous attempts to break down PET have been constrained by factors such as the enzyme’s vulnerability to temperature and sluggish reaction rates.
However, with this robust Hydrolase enzyme, the researchers of UT Austin were able to break down mixed color and clear products of PET completely. The researchers are confident that this new enzyme will withstand temperature variations in non-laboratory conditions.
Researchers said that this plastic degrading enzyme would be able to clean plastic landfills and waste plants when used in sufficient quantities. Less than 10% of the world’s plastic waste is recycled, whereas the rest pollutes the environment, water, air, and food chain.
Researchers used an ML model to generate mutations of PETase that allow the degradation of PET. The model predicted mutations in these enzymes that would accomplish the goal of quickly depolymerizing waste plastic at low temperatures. To prove the effectiveness of the Hydrolase enzyme, researchers demonstrated that FAST-PETase was able to almost completely degrade untreated, postconsumer-PET from 51 different thermoformed products in one week.
