Multinational retail corporation Walmart announces that it plans to offer artificial intelligence-powered predictive clinical recommendations to its employees.
Walmart has collaborated with machine learning-enabled healthcare service providing company Health at Scale Technologies to develop and offer the recommendation service to workers.
Walmart employees and their families who are enrolled in the company’s health plan and work in places where Health at Scale is available will benefit from the new software, which will provide personalized clinician recommendations.
However, the companies did not provide any information regarding where the service will be available. The newly developed technology would considerably help provide better perks to employees and their families.
Vice President of Walmart US benefits division, Lisa Woods, said, “Customizing services and treatments to individual needs is the next frontier in healthcare and is a major part of Walmart’s commitment to helping associates and their family members find great doctors who consistently deliver the best value and quality care in their community.”
She further added that they are pleased to partner with Health at Scale and are looking forward to seeing how this new benefit will affect associates’ healthcare experiences and outcomes. According to the companies, the idea is to make it easier for plan members to locate doctors who are most suited to their health requirements and history of care.
United States-based technology company Health at Scale was founded by a group of leading machine learning and clinical faculty, David Guttag, John Guttag, Mohammed Saeed, and Zeeshan Syed, in 2015. To date, the company has raised $16 million in its series A funding round from investors like Optum.
Health at Scale specializes in developing solutions for optimizing care delivery for individuals by empowering at-risk payers, employers, and providers using modern technologies like artificial intelligence and machine learning.
CEO of Health at Scale, Zeeshan Syed, said, “Finding the right provider is one of the most important health decisions a patient makes. It is also one of the hardest. What we really need to optimize is the patient-provider match.”
Technology giant Meta announces that it might shut down the operation of its social media platform Facebook in Europe. This information was revealed in the company’s annual report for the Securities and Exchange Commission.
The European data regulations norms do not allow Meta to store user data in servers located in the United States, causing this new trouble for the company.
Meta claims that the capacity to process user data across borders is critical to its company, both in terms of operations and ad targeting. In recent years, the European Union has taken multiple measures to prioritize the data privacy of users for providing a secure web experience to citizens.
Meta’s statement mentioned, “If we are unable to transfer data between and among countries and regions in which we operate, or if we are restricted from sharing data among our products and services, it could affect our ability to provide our services, the manner in which we provide our services or our ability to target ads.”
It further mentioned that a number of our most essential products and services, such as Facebook and Instagram, will very likely be unavailable in Europe. Meta highlighted a 2020 judgment by the Court of Justice of the European Union (CJEU), which provided a framework for data transfers from Europe to the United States.
Last year, the European Court of Justice declared this treaty null and void because of data protection violations. After this decision of the European Court of Justice, both sides have stated that they are working on a new or amended deal.
Apart from Privacy Shield, Meta also uses so-called model agreements, or Standard Contractual Clauses, as the principal legal basis for processing data from European users on American servers.
Energy-efficient artificial intelligence-powered supercomputing components developing company Ceremorphic announces that it plans to launch a new 5nm-based microchip.
According to Ceremorphic, the new chipset will be highly scalable and less power-consuming, which would deliver better performance, efficiency, security, and reliability. The technology firm says that the new processor will be able to handle next-generation applications such as AI model training, HPC, automotive processing, drug discovery, and metaverse processing.
Ceremorphic has an advantage over other chip firms since it has access to TSMC’s 5-nanometer process node. The company plans to use 2024 for its Hierarchical Learning Processor.
CEO and Founder of Ceremorphic , Venkat Mattela, said,” Having developed many innovations in multi-thread processing, algorithm-driven VLSI, reliable performance circuits, low-energy interface circuits, quantum-resistant security microarchitecture, and new device architectures beyond CMOS, Ceremorphic is well on its way to accomplishing our goals.”
He further added that the issues this market faces with reliable performance computing require an entirely new architecture built specifically to ensure dependability, security, energy efficiency, and scalability.
This new development will now allow Ceremorphic to compete with several global giants like NVIDIA, which is a developer of processors used for artificial intelligence and high-performance computing. The processor will come with multiple top-notch features like –
Custom Machine Learning Processor and custom FPU clocked at 2Ghz.
ThreadArch based RISC –V processor for proxy processing.
Open AI framework software support with optimized compiler and application libraries.
United States-based technology firm Ceremorphic was founded by Venkat Mattela in 2020. The enterprise specializes in developing an architecture that is used for multiple purposes, including AI model training, HPC, drug discovery, metaverse processing, and many more. To date, the company has raised nearly $5 million in its recently held series A funding round.
Professor of Electrical Engineering and Computer Science at Stanford University, Subhasish Mitra, said, “Reliable performance computing is absolutely something this industry needs, and the approach that Ceremorphic is pursuing is a significant step in the right direction.”
Reliance’s telecommunication service providing brand Jio invests $15 million in deep learning and artificial intelligence startup Two Platforms. The investment has made Reliance Jio a 25% equity stakeholder on a fully diluted basis.
United States-based artificial intelligence company Two Platforms was founded by Pranav Mistry recently in 2021. White & Case served as the legal advisor for Jio for the transaction. Two platforms specialize in developing interactive and immersive artificial intelligence experiences beyond text and voice.
The company’s founding team has extensive experience in research, design, and operations with some of the world’s most prestigious technological organizations. The company’s platform enables artificial intelligence-powered voice and video chats in real-time, as well as artificial humans, immersive environments, and vivid games.
The investment would allow Two Platforms to be able to work alongside Jio to accelerate the adoption of new technologies and develop disruptive technologies such as artificial intelligence solutions, metaverse, and mixed realities.
Director of Jio, Akash Ambani, said, “We are impressed with the strong experience and capabilities of the founding team at TWO in the areas of AI/ ML, AR, metaverse and Web 3.0. We look forward to working together with TWO to help expedite the development of new products in the areas of interactive AI, immersive gaming, and metaverse.”
According to a statement made by the company, TWO intends to use its interactive AI technology in consumer applications first, followed by entertainment and gaming, as well as enterprise solutions such as retail, services, education, and health and wellness.
“Jio is foundational to India’s digital transformation. We at TWO are excited to partner with Jio to push the boundaries of AI and introduce applications of Artificial Reality to consumers and businesses at scale,” said the CEO of Two Platforms, Pranav Mistry.
Internet solutions and web browser developing company Mozilla closes its Virtual Reality (VR) browser named Firefox Reality. The browser was specifically developed to be used in a VR environment.
However, the company has kept the code of the browser open source. Igalia, a software firm, is working on a browser based on the source code of Firefox Reality. Igalia plans to release its new VR browser named Wolvic by the end of next week.
Firefox Reality was launched in 2018 and was live for four years. Earlier the VR browser was available on Viveport, Oculus, Pico, and HoloLens platforms through their app stores. It was capable of performing various unique tasks.
The browser eliminated the need of operating a mouse as it could access URLs, do searches, and browse 2D and 3D internet using a virtual reality hand controller.
Mozilla mentioned, “Today, we’re delighted to announce that the Firefox Reality browser technology will continue under Igalia, where they will uphold the same principles we started when we created Firefox Reality — an open-source browser that respects your privacy.”
According to Igalia, Wolvic will be available for AOSP-based stand-alone XR devices, and HarmonyOS tethered systems like Oculus, Huawei VR Glass, Vive, Pico, and Lynx. The software company has already raised partial funding to run the newly announced virtual reality browser.
“As of Today, Igalia has secured partial funding over the next two years and will continue to invest ourselves. However, to be really successful and build a healthier ecosystem, we know that we’ll need to find additional partners,” mentioned Igalia in a statement.
Interested investors can reach out to the company for further details. Users can still find the Firefox Reality browser in several VR app stores, but the company said that they would remove it soon.
Global semiconductor manufacturing giant Intel, the Ministry of Electronics and Information Technology (MeitY), National e-Governance Division (NeGD), the Government of India, and the United Nations Development Programme (UNDP) recently held a session on deep learning.
It was their seventh session which focused on Demystifying Deep Learning. More than 95 government officials and ten ministers attended the online session meant for government staff and policymakers.
Intel specialists participated in the recently held webinar targeted for policymakers as part of the company’s Digital Readiness portfolio. The webinar provided relevant industry experience and used case studies for helping policymakers.
Abhishek Singh, President and CEO of NeDGD, Ministry of Electronics and Information Technology, said, “These sessions are not only leading to capacity building but also more and more adoption of technology-based solutions in collaboration with industry experts and ecosystem partners, walking the participants through relevant use-cases and discussing scalable solutions.”
He also talked about several artificial intelligence technologies like Netra.ai, an AI-based treatment for diabetic retinopathy, as well as E-Paarvai, a tool used by the Tamil Nadu government to diagnose cataracts. During the event, participants also got the opportunity to take part in an interactive and hands-on artificial Intelligence exercise via a gaming interface. A
ccording to officials, the session will host several other sessions regarding emerging technologies like blockchain and others. The prime aim of the sessions will be to provide industry-specific experience to attendees with international and Indian use cases.
“In the coming times, Deep Learning Networks will help us understand computer memory better. DL will be democratized further to become a standard part of a developer’s toolkit,” said Omesh Tickoo, Principal Engineer and Research Manager at Intel. He also talked about the rising demand for artificial intelligence and machine learning technologies for modernizing robots and other systems.
With its newest innovation, DeepMind has again pushed the boundaries of artificial intelligence capabilities. This British AI subsidiary of Alphabet has created an AI-backed system called AlphaCode. DeepMind claims that the system can generate “computer programs at a competitive level.” DeepMind discovered that when it tested its system against coding tasks used in human contests, it received an “estimated rank” that placed it among the top 54 percent of human coders.
Image Credits: DeepMind
AlphaCode isn’t the first AI tool to produce computer code. Microsoft unveiled a similar tool (Copilot) to help programmers in June, built with the support of GitHub and OpenAI. The GitHub Copilot is a tool used to analyze existing code and generate new snippets or autocompletes lines of code, rather than acting as a standalone problem-solving entity.
However, these models still fall short when tested against more difficult, unknown issues that need problem-solving skills beyond translating instructions into code. Researchers discovered that roughly 40% of Copilot’s output included security flaws in one investigation. As per Armin Ronacher, creator of the Flask web framework for Python, Copilot can be prompted to recommend copyrighted code from the 1999 computer game Quake III Arena, accompanied with comments from the original programmer.
— Armin “vax ffs” Ronacher (@mitsuhiko) July 2, 2021
“I’m leaving GitHub because copilot uses my OpenSource code for training” is such an odd move. Anyone can fork it to there and GitHub can feed OpenSource code from anywhere to it and US copyright law permits this. I’m also pretty certain we should not strengthen copyright laws …
— Armin “vax ffs” Ronacher (@mitsuhiko) July 3, 2021
At the time of Copilot’s debut, GitHub revealed that roughly 0.1% of its code suggestions might contain fragments of verbatim source code from the training set. Copilot could even potentially generate true personal data like phone numbers, email addresses, or names, as well as code that is biased or racist in nature. As a result, the company recommends that the code be thoroughly inspected and verified before being used. The problem of generating meaningless codes is also common to GPT-3.
However, DeepMind claims that Alphacode, unlike most large model NLP tools, is a large-scale transformer code generation model that can provide unique solutions to these deeper-thinking challenges. While designing AlphaCode, DeepMind focused on the following three objectives:
Finding a clean dataset to work with and since coding competitions are plentiful, the data was easily acquired.
Developing an efficient algorithm, similar to the transformer-based architectures used in natural language processing and image recognition.
Making numerous example solutions and then filtering them to locate work that is relevant to the problem at hand.
The emphasis was given to building transformer-based neural architecture because, they can usually learn in a semi-supervised environment, with unsupervised pretraining and supervised fine-tuning. Transformers are initially exposed to “unknown” data for which no previously specified labels exist in this situation. Then they are trained on labeled datasets throughout the fine-tuning phase to learn to do specific tasks like answering queries, assessing sentiment, and paraphrasing documents.
They do agree, though, that AlphaCode’s abilities aren’t precisely reflective of the kind of issues that a typical programmer may encounter for the time being. AlphaCode was not designed to address the same types of problems that an average programmer faces. It’s also worth noting that the major goal of AlphaCode AI’s development, which was not intended to replace software engineers, is to assist those who wish to code.
According to Oriol Vinyals, Principal research scientist at DeepMind, “the research was still in the early stages, but the results brought the company closer to creating a flexible problem-solving AI.”
DeepMind produced AlphaCode by training a neural network on a large number of coding samples gathered from GitHub’s software repository in the programming languages C++, C#, Go, Java, JavaScript, Lua, PHP, Python, Ruby, Rust, Scala, and TypeScript. In addition, DeepMind fine-tuned and tested the AlphaCode system using CodeContests, a new dataset the lab constructed that combines public programming datasets with challenges, answers, and test cases collected from Codeforces. With 41.4 billion parameters, AlphaCode generates multiple solutions in the C++ and Python programming languages when given a new problem to solve. After that, the DeepMind team executed debugging and testing to automatically select those programs to identify ten solutions worth evaluating and possibly submitting outside.
AlphaCode was evaluated against ten challenges curated by Codeforces, a competitive coding site that offers weekly tasks and assigns coders ranks akin to the Elo rating system used in chess. These tasks are not the same as those that a coder could encounter, e.g., working on a commercial app. They’re more self-contained and need a broader understanding of both algorithms and theoretical computer science ideas. In short, solving these advanced puzzles needs a perfect blend of logical reasoning, coding, critical thinking, and understanding natural language. Further, each content had more than 5,000 participants on the Codeforces site. Averaging at within the top 54.3% of responses, DeepMind estimates that this gives AlphaCode, a Codeforces Elo of 1238, which places it within the top 28% of users who have competed on the site in the last six months. Meanwhile, on CodeContests, given up to a million samples per problem, AlphaCode solved 34.2% of problems.
An example interface of AlphaCode tackling a coding challenge. The input is given as it is to humans on the left and the output generated on the right. Image Credit: DeepMind
Mike Mirzayanov, the founder of Codeforces, argues that the AlphaCode outcomes exceeded his expectations. However, Mirzayanov admitted that he was originally unsure since the method has to be implemented even in basic competitive scenarios. Furthermore, it is critical to even invent it.
At the same time, DeepMind believes it has to address several critical issues before AlphaCode is SaaS-ready. These include interoperability, bias, generalization, and security concerns. Further, as common with all large-scale models, training this transformer-based code generator will need a significant amount of compute. On the plus side, unlike neural network models, which normally require accelerators, once AlphaCode has generated a program, it can usually be performed inexpensively by any computer. This also implies that it might be more conveniently scaled to cater to various applications.
DeepMind, which Google acquired in 2014, has made headlines for projects like AlphaGo, which defeated the world champion in the game of Go in a five-game match, and AlphaFold, which solved a 50-year-old grand challenge in biology. With AlphaCode, the company is set to bring another revolutionary milestone in problem-solving AI technologies.
Business Insider reported that Meta, formerly known as Facebook, blames Apple for its historic less revenue generation in the last quarter.
According to the company, it would lose $10 billion in a year because of a software change made by Apple. A software update allows iPhone users to choose which apps they want to track their behavior across other applications, which led to this massive loss of Facebook.
This feature allowed users to unselect Facebook from that list, considerably affecting Facebook’s advertising revenue. CFO of Meta, David Wehner, said, “The impact of iOS overall as a headwind on our business in 2022 is on the order of $10 billion.”
Advertisements are one of the prime sources of revenue for Meta, and when iPhone users got the chance to disallow Facebook to track them, Meta lost its primary revenue generation source from Apple users.
The new feature was released with the launch of Apple’s iOS 14.5 software update in April last year. A recent report claims that around 95% of iOS users having the feature chose to disallow Facebook from tracking them, which was a significant factor for the loss of Meta.
“We can’t be precise on this. It’s an estimate. We’re working hard to mitigate those impacts and continue to make ads relevant and effective for users,” said David Wehner.
Because of the functionality, app publishers were bound to include a pop-up asking for permission to track user activity for ad sales. When users choose not to share their information with a particular application, Apple disables that app’s access to a range of data that advertisers utilize.
Researchers from the National Eye Institute have developed a new artificial intelligence-powered system that can accurately evaluate vision loss in individuals. The system can monitor the risk of an eye disease named Stargardt that causes vision loss in children.
The artificial intelligence system identifies the retina cells affected by the disease to generate data, helping in patient monitoring. Apart from generating information related to their health condition, the system also helps identify genetic causes of the illness and develop proper treatment plans.
Michael F. Chiang, MD, director of the NEI, said, “These results provide a framework to evaluate Stargardt disease progression, which will help control for the significant variability from patient to patient and facilitate therapeutic trials.”
The researchers focused on the health of photoreceptors in the ellipsoid zone, a characteristic of the inner/outer segment border of photoreceptors that is decreased or eliminated because of the disease.
The most commonly found form of Stargardt is known as ABCA4-associated retinopathy, which develops in nearly out of every 9000 individuals. This disease develops because some individuals inherit two mutated copies of the ABCA4 gene from their parents.
Whereas people who inherit only one gene are genetic carriers of the disease, but they don’t develop it. Researchers used a deep-learning algorithm to quantify and compare photoreceptor loss and different layers of the retina based on the patient’s phenotype and ABCA4 variation.
Researchers studied 66 such patients for a period of five years to develop this system. Images were clicked of their retinas and were fed to a deep learning algorithm to generate results.
Brian P. Brooks, MD, Ph.D., chief of the NEI Ophthalmic Genetics & Visual Function Branch, said, “Different variants of the ABCA4 gene are likely driving the different disease characteristics, or phenotypes. However, conventional approaches to analyzing structural changes in the retina have not allowed us to correlate genetic variants with phenotype.”
Turkey’s Ministry of Agriculture and Forestry is now planning to use artificial intelligence solutions to tackle the massive challenge of controlling wildfires.
The ministry said they intend to use AI for detecting early signs of smoke, which would help in taking adequate precautionary measures against the spread of wildfires. The artificial intelligence-powered system will be developed by the ministry that would use a range of cameras for smoke detection.
This development comes after the massive destruction Turkey had to bear last year due to a sudden spread of wildfire in the region. According to an interview published by Yeni Şafak newspaper, the cameras will be installed at the top of watch towers located in forests to increase the system effectiveness and accuracy.
According to Forestry Minister Bekir Pakdemirli, “smoke perception” allows cameras to detect smoke from a distance of up to 20 kilometres. Additionally, the artificial intelligence-powered system would drastically reduce the fire detection time to a matter of a few minutes, which if done with current practices, would be time consuming.
Currently, the technology is deployed in two provinces named Antalya and Mula, which suffered heavy losses during the fire outbreak of 2021. Last year, Turkey was ravaged by a catastrophic wildfire along Anatolia’s southern coast, which engulfed 53 provinces and caused over 270 forest fires.
Therefore this new AI system will be used as an effective precautionary tool, helping officials to take quicker actions. “AI enables us to keep track of the smoke and deploy our teams as soon as possible,” said Bekir Pakdemirli.
When cameras detect smoke, they automatically convey alert signals to authorities through text or video message using artificial intelligence and machine learning. Turkey currently has more than 100 watch towers with cameras, in which each tower will be able to scan an area of 50,000 hectare and send required notifications to authorities in under two minutes. Recently,the President of Turkey, Recep Tayyip Erdoğan mentioned that the government plans to accelerate the development and deployment of safety infrastructures in the country to battle wildfires. President Erdoğan, said, “We will increase the number of domestically manufactured unmanned aerial vehicles (UAVs) to eight, the number of firefighting planes to 20 and helicopters to 55”
:no_upscale()/cdn.vox-cdn.com/uploads/chorus_asset/file/23212711/Fig_04_Final.jpg)
