As wildfires currently devastate western North America, a new airborne project team hopes to develop a space solution to stop conflagrations before they get out of control. The project could one day help future firefighters acquire "fire behavior" maps within 20 minutes of an outbreak, using satellite data combined with machine learning (a kind of artificial intelligence), according to a statement from the University of California, Berkeley. The project, funded by a $1.5 million grant, will fund "spotter planes" with infrared detectors -- heat-seeking sensors to examine flame length and geometry to learn more about how fires spread. Meanwhile, machine learning algorithms -- provided they are trained well on other "hot spot" datasets -- could spot new fires in the region within milliseconds, to send alerts. If all goes well in airborne testing, the detector team -- which includes UC Berkeley's Space Sciences Laboratory and Nevada-based fire assessment company Fireball Information Technologies -- hopes to send similar sensors to space within four years to make monitoring and discovery a 24/7 activity.

In general, trading is about making decisions on transactions with assets in order to make a profit. All technical analysis is based on statistical data, past market behavior, and reactions. Consequently, the analysis and search for some market patterns can be performed not only by person but by computer and artificial intelligence. It is no secret that trading robots have been working in the stock market for a long time, focusing on price movements in trends and within channels. According to a 2020 JPMorgan study, over 60% of trades over $10M were executed using algorithms.

All the sessions from Transform 2021 are available on-demand now. DeepMind and the European Bioinformatics Institute (EMBL), a life sciences lab based in Hinxton, England, today announced the launch of what they claim is the most complete and accurate database of structures for proteins expressed by the human genome. In a joint press conference hosted by the journal Nature, the two organizations said that the database, the AlphaFold Protein Structure Database, which was created using DeepMind's AlphaFold 2 system, will be made available to the scientific community in the coming weeks. The recipe for proteins -- large molecules consisting of amino acids that are the fundamental building blocks of tissues, muscles, hair, enzymes, antibodies, and other essential parts of living organisms -- are encoded in DNA. It's these genetic definitions that circumscribe their three-dimensional structures, which in turn determine their capabilities.

Mobileye received a special permit from New York state, allowing manufacturers of "autonomous vehicle technology" to test on public streets. The permit requires that drivers be present in the vehicle but allows them to keep their hands off the steering wheel yet "be prepared to take control when required to … operate the vehicle safely and lawfully." It's unclear whether others may have applied. The state hasn't responded to a request for comment.

These are some of the outcomes that AI developers fear will come from their work, according to a new report issued today by the Deloitte AI Institute and the U.S. Chamber of Commerce. Titled "Investing in trustworthy AI," the 82-page report from Deloitte and the Chamber Technology Engagement Center sought to identify the concerns that technology experts have when it comes to the adoption of AI, as well as highlight the impact that government investment in AI can have on the emerging technology. Algorithmic bias and a lack of humans in decision loops are concerns for about two-thirds of the 250 people who participated in the survey. Another 60% identified "rogue or unanticipated behavior" of autonomous agents as a threat, while 56% said the lack of explainability of algorithms was a concern. "Perceived, and actual, discrimination by AI systems undermines the confidence individuals have in whether they are being given a fair opportunity when AI is involved," the report stated.

In collaboration with academic researchers in China, Alibaba has developed a search engine simulation AI that uses real world data from the ecommerce giant's live infrastructure in order to develop new ranking models that are not hamstrung by'historic' or out-of-date information. The engine, called AESim, represents the second major announcement in a week to acknowledge the need for AI systems to be able to evaluate and incorporate live and current data, instead of just abstracting the data that was available at the time the model was trained. The earlier announcement was from Facebook, which last week unveiled the BlenderBot 2.0 language model, an NLP interface that features live polling of internet search results in response to queries. The objective of the AESim project is to provide an experimental environment for the development of new Learning-To-Rank (LTR) solutions, algorithms and models in commercial information retrieval systems. In testing the framework, the researchers found that it accurately reflected online performance within useful and actionable parameters.

Corporations are going through rough times. The times are uncertain, and having to make customer experiences more and more seamless and immersive isn't taking off any of the pressure on companies. In that light, it's understandable that they're pouring billions of dollars into the development of machine learning models to improve their products. Companies can't just throw money at data scientists and machine learning engineers, and hope that magic happens. Here's how AI can improve your company's customer journey The data speaks for itself.

The Pentagon sees artificial intelligence as a way to outfox, outmaneuver, and dominate future adversaries. But the brittle nature of AI means that without due care, the technology could perhaps hand enemies a new way to attack. The Joint Artificial Intelligence Center, created by the Pentagon to help the US military make use of AI, recently formed a unit to collect, vet, and distribute open source and industry machine learning models to groups across the Department of Defense. A machine learning "red team," known as the Test and Evaluation Group, will probe pretrained models for weaknesses. Another cybersecurity team examines AI code and data for hidden vulnerabilities.

Big road projects will often uncover historic finds. During the £1.5bn upgrade of the A14 in Cambridgeshire, an archaeologist found what was believed to be the earliest evidence of beer brewing in Britain, dating back around 2,000 years. Generating as much excitement, for different reasons, was the introduction of a very modern concept on the same scheme. The project team pioneered artificial intelligence (AI) and machine-learning technology to successfully predict times when an accident was more likely to happen – and to take action to stop it. By collecting swathes of information and using the AI, data scientists were able to spot problems before they occurred.

All the sessions from Transform 2021 are available on-demand now. The majority of organizations agree that computer vision has the potential to transform key areas of business, but only 10% are using it today. That's according to an IDG survey commissioned by Insight, a business-to-business technology consultancy, which asked 200 IT leaders about their awareness, adoption, and perceptions of computer vision. Computer vision is a type of AI technology that allows machines to understand, categorize, and differentiate between images. Using photos from cameras and videos as well as deep learning components, computer vision can identify and classify objects and then react to what it "sees."