How do you make music sound cool? How can you explain why the soundtrack to, say, any of the spy films of the 1960s still sound cool decades later? How can Foxy Lady still get into your body and make you groove so much, 55 years since Jimi Hendrix laid down those slick licks? Jungle, Thundercat or Lil Nas X might be able to tell you – but so can the people behind the soundtrack to Deathloop, one of the most effortlessly classy-looking (and sounding) games in years. Arkane's mind-bending sci-fi thought experiment is a masterclass in cool.

The pandemic ushered us all into this experience together, and now that we have "graduated" into a post-pandemic world, we can reflect on the lessons it has taught us. The lessons include living and working together to create a world and organisation unified by a purpose. It also helped shake the mindset of the global economy as we strived to make the impossible possible. Instead of employees following a fixed schedule, it has transitioned to employees being on-demand as per their choice of time and place. As a result, employees are now free from the office walls - it is limitless, and they can meet the needs of their companies and consumers.

Today, nearly everyone accepts that in order to slow the damage we are doing to our planet and environment, humans must transition away from the use of fossil fuels. This has led to many science and business innovations as we search for new sustainable or renewable alternatives to coal, oil, and gas. Although it would be nice to think everyone wants to do their part in order to save the world, there are strong financial incentives too. The value of the renewable energy market is set to grow from $880 billion to nearly $2 trillion by 2030. And the growing awareness of the importance of environmental and social governance (ESG) issues means there are tremendous political incentives, too.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.S. troops in Syria were targeted Thursday with a "deliberate and coordinated" drone attack, military officials told Fox News, saying that the U.S. has the "inherent right of self defense" and will "respond at a time and place of our choosing." Bill Urban confirmed that the al-Tanf Garrison area "was subjected to a deliberate and coordinated attack." "Based on initial reports, the attack utilized both unmanned aerial systems and indirect fire," Urban said.

Princeton, New Jersey--(Newsfile Corp. - December 3, 2019) - NotForgotten Digital Library LLC, creators of the first digital time capsule powered by blockchain, today announced their use of Veritone aiWARE and Digital Media Hub. Veritone's cognitive capabilities will add a new dimension to NotForgotten's current offerings by adding automated video transcription and metadata creation to make large volumes of video content easily searchable. Videos within NotForgotten archives can then be easily accessed through the custom-branded Digital Media Hub and users can discover insights about a set time and place in history. "The new capabilities Veritone is adding to NotForgotten's archives are set to change the way future historians study our present," says Adrienne Liebenberg, co-founder at NotForgotten. "Veritone aiWARE will analyze the videos within the NotForgotten Digital Library and add AI-enabled insights about society and people within a set time and place. This will be the first time we - society at large - will have the framework in place to not only record and research history from a personal perspective, but to also see how these events made people feel."

Given how deadly and destructive lightning can be, it would certainly be good to know in advance where and when it was going to strike. A new artificial intelligence-based system could help, utilizing nothing but standard weather-station data. Developed by a team from the Electromagnetic Compatibility Laboratory at Switzerland's EPFL research institute, the system was "trained" using a database of readings of four basic weather parameters: atmospheric pressure, air temperature, relative humidity and wind speed. Gathered over a 10-year period from 12 Swiss weather stations in urban and mountainous regions, these readings were cross-referenced with recordings from lightning detection and location systems. This allowed the AI algorithms to learn which weather conditions were associated with lightning strikes in given areas.

A diverse team is also key to avoid and detect bias, discriminatory views and prejudiced opinions reflected in the data the systems are learning from. Googles head of AI, John Giannandrea, recently said "forget killer robots -- bias is the real AI danger". I certainly agree with him. The AI systems learn from data. It all starts with data.

Stanford University's "One Hundred Year Study on Artificial Intelligence" report from 2016 is close to the truth. The study's expert panel expects to see AI integrated into industries like elder care, education and public safety by 2030. "The field of AI is shifting toward building intelligent systems that can collaborate effectively with people." Tech companies are, of course, trendsetters when it comes to new technologies, and AI is no different. In fact, Google has been using AI through an algorithm it calls RankBrain, a machine-learning way to sort through all those Google Search results.

Our attitude towards robots and automated systems seems to fall into two buckets. Either they'll be unthinking machines that do our every bidding, or they'll take the forms runaway intelligence that rapidly decide that they can do without humans. As with so much, the answer probably lies somewhere in between, and whilst most of the time it's only right for the machine to do as they're told, there will be instances where they will need to think for themselves and disobey. For instance, nursing robots may be ordered to give their patient some medication, but the machine knows that the medication had already been given that day and an overdose could be harmful. There are many instances such as that where robotic disobedience would be helpful, but programming such interactions is not easy.

For my submission to HackCambridge I wanted to spend my 24 hours learning something new in accordance with my interests. I was recently introduced to protein interaction networks in my Bioinfomartics class, and during my review of machine learning techniques for an exam noticed that we study many supervised methods, but no unsupervised methods other than the k means clustering. Thus I decided to combine the two interests by clustering the Protein interaction networks with unsupervised clustering techniques and communicate my learning, results, and visualisations using the Beaker notebook. The study of protein-protein interactions (PPIs) determined by high-throughput experimental techniques has created karge sets of interaction data and a new need for methods allowing us to discover new information about biological function. These interactions can be thought of as a large-scale network, with nodes representing proteins and edges signifying an interaction between two proteins.