Palo Alto Networks has released next-generation firewall (NGFW) software that integrates machine learning to help protect enterprise traffic to and from hybrid clouds, IoT devices and the growing numbers of remote workers. The machine learning is built into the latest version of Palo Alto's firewall operating system – PAN 10.0 – to prevent real-time signatureless attacks and to quickly identify new devices – in particular IoT products – with behavior-based identification. NGFWs include traditional firewall protections like stateful packet inspection but add advanced security judgments based on application, user and content. "Security attacks are continually morphing at rapid pace and traditional signature-based security approaches cannot keep up with the millions of new devices, running a variety of operating systems and software stacks coming on the network," said Anand Oswal senior vice president and GM at Palo Alto. "IoT devices, which are growing exponentially, exacerbated that issue because they have so many of their own different agents, patches and OS's it's impossible to set security policies around them." Oswal said the ML in its new NGFW uses inline machine-learning models to identify variants of known attacks as well as many unknown cyberthreats to prevent up to 95% of zero-day malware in real time.

Today, large resources are used to provide vessels in the polar seas with warnings about the spread of sea ice. Artificial intelligence may make these warnings cheaper, faster, and available for everyone. For vessels that journey into the polar seas, keeping control of the spread of sea ice is critical, which means that large resources are spent to collect data and determine future developments to provide reliable sea ice warnings. "As of now, large resources are needed to create these ice warnings, and most of them are made by The Norwegian Meteorological Institute and similar centers," says Sindre Markus Fritzner, a doctoral research fellow at UiT The Arctic University of Norway. He is employed at the Department of Physics and Technology and has recently submitted a doctoral thesis in which he looked at the option of using artificial intelligence to make ice warnings faster, better, and more accessible than they are today.

Data quality concerns continue to plague artificial intelligence. But blockchain-based incentive ... [ ] mechanisms could significantly change that. The Covid-19 outbreak has overwhelmed health systems around the world. At a point, bed spaces and ventilators for patients as well as protective gear for health workers were not enough to go around. This meant that health systems, especially in developed countries, had to employ certain technologies to allocate resources efficiently.

YouTuber Funk Turkey has made a new fake Nirvana song using A.I. programming. It's Turkey's latest endeavor following a flurry of other mock tracks mimicking the likes of AC/DC, Metallica and Nickelback, to name a few. The song is called "Smother," a three-minute concoction of Nirvana-esque tropes that attempts to mirror the legendary band's distinctive personality. Turkey notes in the video's description, "using lyrics.rip to scrape the Genius Lyrics Database, I made a Markov Chain write Nirvana lyrics." Generating lines like "I could eat your heart-shaped box for food" and "You never lost control," the lyrics reappropriate familiar keywords from some of Nirvana's biggest hits.

There is no doubt that the artificial intelligence (AI) phenomenon will have a profound impact on businesses large and small this year; that part is easy to predict. What impact it will have, and whether this is a good or a bad thing, is harder to tell. Let's start with the basics of AI. In their PwC briefing, Chris Curran and Anand Rao explained it this way: "In our broad definition, AI is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they're sensing and their objectives. Forms of AI in use today include, among others, digital assistants, chatbots, and machine learning. Helping people to perform tasks faster and better. Helping people to make better decisions. Automating decision making processes without human intervention."

Tiktok has revealed greater insight into how its recommendation algorithm works in a new blog post. The algorithm uses a number of factors to base its suggestions of 15-second clips. Theses include user interactions, such as the videos a user likes or shares, accounts they follow, and comments posted as well as video information such as sounds and hashtags. Tiktok will use your device and language settings to "make sure the system is optimised for performance" but receive lower weight compared to the other metrics "since users don't actively express these as preferences." Many of TikTok's algorithm influences will be familiar; similar features are offered by other social media sites such as Twitter and YouTube upon setting up a new account.

The Atlanta Botanical Garden will be using a robotic sloth to save some of the world's most endangered species. The sloth robot, called Slothbot, hangs in trees to monitor animals, plants, and the environment. It was built by the robotics engineers at the Georgia Institute of Technology and uses solar panels to power itself. In larger environments, Salothbot will be able to switch between cables to cover more ground. "SlothBot embraces slowness as a design principle," the Georgia Tech "That's not how robots are typically designed today, but being slow and hyper-energy efficient will allow SlothBot to linger in the environment to observe things we can only see by being present continuously for months, or even years."

Reviewed by Emily Henderson, B.Sc.Jun 15 2020 Researchers have used artificial intelligence to reduce the'communication gap' for nonverbal people with motor disabilities who rely on computers to converse with others. The team, from the University of Cambridge and the University of Dundee, developed a new context-aware method that reduces this communication gap by eliminating between 50% and 96% of the keystrokes the person has to type to communicate. The system is specifically tailed for nonverbal people and uses a range of context'clues' – such as the user's location, the time of day or the identity of the user's speaking partner – to assist in suggesting sentences that are the most relevant for the user. Nonverbal people with motor disabilities often use a computer with speech output to communicate with others. However, even without a physical disability that affects the typing process, these communication aids are too slow and error prone for meaningful conversation: typical typing rates are between five and 20 words per minute, while a typical speaking rate is in the range of 100 to 140 words per minute.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Would you trust an artificial intelligence algorithm that works eerily well, making accurate decisions 99.9 percent of the time, but is a mysterious black box? Every system fails every now and then, and when it does, we want explanations, especially when human lives are at stake. And a system that can't be explained can't be trusted. That is one of the problems the AI community faces as their creations become smarter and more capable of tackling complicated and critical tasks.

In a study earlier this year accepted to the Genetic and Evolutionary Computation Conference (GECCO) 2020, Google researchers investigate the properties of AI software agents that employ self-attention bottlenecks. They claim that these agents not only demonstrate an aptitude for solving challenging vision-based tasks, but that they're better at tackling slight modifications of the tasks, due to their blindness to details that might confuse them. Inattentional blindness is the phenomenon that causes a person to miss things in plain sight; it's a consequence of selective attention, a mechanism that's believed to enable humans to condense information into a form compact enough for decision-making. Luminaries like Yann LeCun assert it can inspire the design of AI systems that better mimic the elegance and efficiency of biological organisms. The Google researchers' proposed agent -- AttentionAgent -- aims to devote most of its attention to task-relevant elements, ignoring distractions.