The world is up in the clouds (cloud computing) and the fourth industrial revolution is transforming our lives, society, and our work. While it is making things easy and accessible, it comes with its own perils like cyberattacks. The need to make the cybersecurity landscape stronger is more than ever as cybercriminals have become more clever. In terms of cyberattacks, we've reached a new low where phishers are using the COVID-19 vaccine rollout to trick people into paying for fake vaccines. Scientists are working day and night to create innovative artificial intelligence and machine learning tools to eliminate evolving exploits.

The United States is leading rivals in development and use of artificial intelligence while China is rising quickly and European Union is lagging, a research report showed Monday. The study by the Information Technology and Innovation Foundation assessed AI using 30 separate metrics including human talent, research activity, commercial development and investment in hardware and software. The United States leads, with an overall score of 44.6 points on a 100-point scale, followed by China with 32 and the European Union with 23.3, the report based on 2020 data found. The researchers found the US leading in key areas such as investment in startups and research and development funding. But China has made strides in several areas and last year had more of the world's 500 most powerful supercomputers than any other nation -- 214, compared with 113 for the US and 91 for the EU.

Update: Perseverance is safe on the surface of Mars! The headline has been updated to reflect the news. There will be one more robot on Mars tomorrow afternoon. The Perseverance rover will touch down just before 1:00 PM Pacific, beginning a major new expedition to the planet and kicking off a number of experiments -- from a search for traces of life to the long-awaited Martian helicopter. Here's what you can expect from Perseverance tomorrow and over the next few years.

Businesses should regard AI cybersecurity predictions with a level of skepticism for some time to come due to the reliability uncertainty of such predictions. How many times have you heard that phrase – or a close variation on it – in the past five years? If you've been paying attention to the industry press, the answer is probably in the dozens, if not the hundreds. The argument generally goes like this. AI and ML technologies allow security professionals to process massive amounts of network surveillance data and can spot malicious or suspicious activity at a much higher rate of accuracy than even the most highly-trained human.

Policymakers will not have all of the answers or expertise to make the best decisions when it comes to regulating AI, but asking better questions is an important step forward. Without having this basic understanding of how AI technologies work, policymakers could become either too forceful in regulating AI, or on the contrary, not do enough to keep us safe and avoid the risk of deploying AI-based systems for mass surveillance for instance. Therefore, what is needed is a renewed emphasis on AI education among policymakers and regulators and an increase in funding and recruitment for technical talent in government, in order for the people making decisions about programmes, funding, and adoption when it comes to AI, to be informed about current developments concerning the technology.

In recent years, the field of data science has been able to access increasingly powerful analysis methods thanks to increasingly high-performance hardware. Google's Tensorflow has been the benchmark for editing machine learning and modeling deep learning methods. It still has the most freedom today. But a wide range of options often creates a high barrier to entry. PyTorch vs TensorFlow – With the 2 years younger, also Python-based, open source package PyTorch, Facebook now wants to knock Tensorflow off its throne. It has been steadily gaining popularity for years due to its simplicity and features.

The Department of Defense adopted its Ethical Principles for Artificial Intelligence in February 2020, a first for any military organization. These principles build on the foundational work performed by the Defense Innovation Board and is tied directly to one of the pillars of the DoD AI Strategy: Leading in military ethics and safety. The Joint Artificial Intelligence Center serves as the Department's lead for coordinating the oversight and implementation of these principles. Alka Patel, head of AI Ethics Policy for the JAIC, focuses on how to operationalize the five DoD AI Ethics Principles (Responsible, Equitable, Traceable, Reliable and Governable) and put them into practice in the design, development, deployment, and use of AI-enabled capabilities. However, to operationalize these principles throughout the DoD, the JAIC is turning to Responsible AI – an enterprise-wide framework that provides the DoD workforce and the American public the confidence that DoD AI-enabled systems will be safe and reliable, and will adhere to ethical standards.

The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized).

Darryl Richardson was delighted when he landed a job as a "picker" at the Amazon warehouse in Bessemer, Alabama. "I thought, 'Wow, I'm going to work for Amazon, work for the richest man around," he said. "I thought it would be a nice facility that would treat you right." Richardson, a sturdily built 51-year-old with a short, charcoal beard, took a job at the gargantuan warehouse after the auto parts plant where he worked for nine years closed. Now he is strongly supporting the ambitious effort to unionize its 5,800 workers because, he says, the job is so demanding and working for Amazon has fallen far below his expectations. Last August, five months after the warehouse opened, Richardson began pushing for a union in what is not only the first effort to organize an entire Amazon warehouse in the United States, but also the biggest private-sector union drive in the south in years. "I thought the opportunities for moving up would be better. I thought safety at the plant would be better," Richardson said. "And when it comes to letting people go for no reason – job security – I thought it would be different."

One of the dramatic trends at the intersection of computing and healthcare has been patients' increased access to medical information, ranging from self-tracked physiological data to genetic data, tests, and scans. Increasingly however, patients and clinicians have access to advanced machine learning-based tools for diagnosis, prediction, and recommendation based on large amounts of data, some of it patient-generated. Consequently, just as organizations have had to deal with a "Bring Your Own Device" (BYOD) reality5 in which employees use their personal devices (phones and tablets) for some aspects of their work, a similar reality of "Bring Your Own Algorithm" (BYOA) is emerging in healthcare with its own challenges and support demands. BYOA is changing patient-clinician interactions and the technologies, skills and workflows related to them. Situations in which patients have direct access to algorithmic advice are becoming commonplace.4