Machine learning is not just about machines. There is still a human element in the loop, and it looks like this will continue for some time. In other words, artificial general intelligence (AGI) is a distant dream. Since humans are interfering in the learning processes of ML models, the underlying biases surface in the form of inaccurate results. Having an unbiased model is almost impossible as humans generate the data, and a model is only as good as the data it is fed.

Trials have begun of a system that will use machine learning to help predict the upcoming demand for intensive care (ICU) beds and ventilators needed to treat patients with COVID-19 at individual hospitals and across regions in England. The COVID 19 Capacity Planning and Analysis System (CPAS), developed by NHS Digital data scientists and a team of researchers from the University of Cambridge, and using data from Public Health England, will support hospitals to plan more accurately and help ensure that resources are deployed to best effect to support COVID-19 throughout the NHS. The first stage alpha trials began this week at four hospitals, aiming to demonstrate the relative accuracy of the system and fine tune it to best meet the needs of hospitals. "With the pressure being placed on intensive care by the current coronavirus pandemic it is essential to be able to predict demand for critical care beds, equipment and staff,"says NHS Digital Chief Medical Officer Professor Jonathan Benger. "CPAS allows individual hospitals to plan ahead, ensuring they can give the best care to every patient. At the same time, the wider NHS can ensure that the ventilators, other equipment and drugs that each intensive care unit will need are in place at exactly the time they are required. In the longer term, it is hoped that CPAS can be used to predict hospital length of hospital stay, discharge planning and wider intensive care demand in the time that will come after the pandemic."

IBM today announced a new tool that taps AI to predict when allergy symptoms are likely to flare up. It's called Allergy Insights with Watson, and it's available in The Weather Channel app for iOS and Android ahead of a launch on the web. In addition to a 15-day forecast that predicts allergy symptom risk (e.g., high, moderate, low) and a 3-day outlook for allergens, Allergy Insights delivers notifications when allergy risk is changing and explanations about how weather conditions can trigger symptoms. It also provides pollen levels by allergen (with mold coming soon), tips for managing allergies or reducing exposure, and news articles and editorial content related to allergies. According to a recent survey conducted by IBM, most allergy sufferers -- 60% -- use weather forecasts to help manage and mitigate the worst of their symptoms. But pollen metrics like tree, grass, and ragweed levels, which the bulk of apps use to assess risk, aren't necessarily good predictors, and their sources tend to be spotty.

Today, the most successful and damaging cyberattacks are executed by highly professional criminal networks rather than "lone-wolf" hackers. These criminal organizations have also become highly adept at leveraging artificial intelligence (AI) and machine learning (ML) tools, making it extremely hard for IT security organizations to keep up -- much less stay ahead of these threats. Cybercriminals are using AI and ML to exploit vulnerabilities such as user behavior or security gaps to gain access to valuable business systems and data. A perfect example of these types of threats are deepfakes – they are realistic, hard to detect and surprisingly easy-to-create facsimiles of real people. Deepfakes have been rightly denounced for the personal harm they inflict through celebrity pornographic videos, the spread of fake news, conspiracy theories, hoaxes and financial fraud.

As AI usage grows, its energy consumption and carbon emissions are becoming an environmental concern. Here's why -- and how we can find solutions. When Mohammad Haft-Javaherian, a student at the Massachusetts Institute of Technology, attended MIT's Green AI Hackathon in January, it was out of curiosity to learn about the capabilities of a new supercomputer cluster being showcased at the event. But what he had planned as a one-hour exploration of a cool new server drew him into a three-day competition to create energy-efficient artificial-intelligence programs. The experience resulted in a revelation for Haft-Javaherian, who researches the use of AI in healthcare: "The clusters I use every day to build models with the goal of improving healthcare have carbon footprints," Haft-Javaherian says.

The global artificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to reach $21.8 billion by 2027; wherein, Asia-Pacific region is expected to register fastest CAGR throughout the forecast period. Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business. In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network.

Getting diagnosed with a sleep disorder or assessing quality of sleep is an often expensive and tricky proposition, involving sleep clinics where patients are hooked up to sensors and wires for monitoring. Wearable devices, such as the Fitbit and Apple Watch, offer less intrusive and more cost-effective sleeping monitoring, but the tradeoff can be inaccurate or imprecise sleep data. Researchers at the Georgia Institute of Technology are working to combine the accuracy of sleep clinics with the convenience of wearable computing by developing machine learning models, or smart algorithms, that provide better sleep measurement data as well as considerably faster, more energy-efficient software. The team is focusing on electrical ambient noise that is emitted by devices but that is often not audible and can interfere with sleep sensors on a wearable gadget. Leave the TV on at night, and the electrical signal – not the infomercial in the background – might mess with your sleep tracker.

While most consumers may find Internet of Things (IoT) devices like Google's Nest or Ring's doorbells new and exciting technology, the manufacturing world has embraced the IoT to optimize discrete and process manufacturing operations for decades. The industrial IoT (IIoT), which started as remote sensing of things like temperature and pressure, has today matured into a way of linking operational systems that control production with the wider world of applications outside of the control room like ERP platforms and supply chain management systems. "The major benefits of the industrial IoT is to bring more visibility to existing processes," said report author Jaques Durand, director of Standards and Engineering at Fujitsu North America and a member of the Industrial Internet Consortium Steering Committee. People want to understand what's going on." Getting to an advanced state of IIoT usage can be difficult without understanding the mistakes to avoid along the way. That's why the Industrial Internet Consortium (IIC), has spent the last six years developing and deploying testbeds for manufacturers to use when evaluating different IIoT technologies, platforms, designs, products, architectures, and use cases. Based on the results of these testbed proofs-of-concept (POC), today the IIC released a white paper, A Compilation of Testbed Results: Toward Best Practices for Developing and Deploying IIoT Solutions, detailing the best practices companies should adopt to ensure successful IIoT deployments. "The IoT problem that each company is facing or each organization is facing is different," Durand said. "Even if they use the same technologies, which is not granted, they are facing very different conditions and priorities in real-world conditions.

The coronavirus (COVID-19) outbreak is having a growing impact on the global economy. So, how is the impact of COVID-19 going to be on the tech job market and what are the latest trends for data science, AI/ML, analytics, IoT, cloud computing? What are the key in-demand tech job profiles and domains during and after the COVID-19 phase? There have been more than 12,750 confirmed cases of COVID-19 in India so far. Between April 6 – 12, 46% and 39% of new confirmed cases have been reported in Europe and the USA respectively.

A new report offers a five-point framework government agencies can use to maximize the benefits of artificial intelligence while minimizing the risks. "Risk Management in the AI Era," released by the IBM Center for the Business of Government April 16, proposes a risk management framework that can help agencies use AI to best suit their needs. "Public managers must carefully consider both potential positive and negative outcomes, opportunities, and challenges associated with the use of these tools," the report states, as well as the relative likelihood of positive or negative outcomes. The framework is based on five criteria. The first is efficiency, which the report defines as the ratio of output generated to input required.