"No longer were there individual destinies; only a collective destiny, made of plague and emotions shared by all." Many more people in Japan can identify that quote now than would have been the case two months ago. In 1947, when Albert Camus' novel "The Plague" first appeared, it was interpreted allegorically. World War II had shattered the world. A plague seemed an all-too-fitting metaphor for man's bleak fate among the ruins.

Artificial intelligence has the potential to greatly simplify our lives – but not everyone is a data scientist and not all data scientists are experts in machine learning. Enter AutoAI – a novel approach of designing, training and optimizing machine learning models automatically. With AutoAI, anyone could soon build machine learning pipelines from raw data directly, without writing complex code and performing tedious tuning and optimization, to then automate complicated, labor-intensive tasks. Several IBM papers selected for the AAAI-20 conference in New York demonstrate the value of AutoAI and different approaches to it in great detail. Most AutoAI research currently focuses on three areas: automatically determining the best models for each step of the desired machine learning and data science pipeline (model selection), automatically finding the best architecture of a deep learning-based AI model, and automatically finding the best hyperparameters (parameters for the model training process) for AI models and algorithms.

Commercial buildings are voracious consumers of energy. The buildings and building construction sectors contribute around 30% of global energy consumption and almost 40% of CO2 emissions (direct and indirect), according to the IEA. Sustainable buildings are more than trendy, they're key to reducing humans' carbon footprint on the planet. Building owners deploy smart building solutions and building-management systems to not only cut operations costs and comply with regulations but also to do their part in reducing carbon emissions without sacrificing occupant comfort in the process. So how do you make an already smart, efficient building smarter and more efficient?

Modern robots are not unlike toddlers: It's hilarious to watch them fall over, but deep down we know that if we laugh too hard, they might develop a complex and grow up to start World War III. None of humanity's creations inspires such a confusing mix of awe, admiration, and fear: We want robots to make our lives easier and safer, yet we can't quite bring ourselves to trust them. We're crafting them in our own image, yet we are terrified they'll supplant us. But that trepidation is no obstacle to the booming field of robotics. Robots have finally grown smart enough and physically capable enough to make their way out of factories and labs to walk and roll and even leap among us.

In keeping with our mission to provide comprehensive advertising analysis, MediaRadar puts together a report of the most important mergers and acquisitions news each week. Stay in the loop, whether you sell advertising space or focus on business development. This week, Gilead takes out FortySeven, Apple acquires start-up Voysis and Infor is purchased by Koch Industries. The American biotechnology company, Gilead has completed an acquisition of Forty Seven, Inc. at a rate of $97.50 per share that equates to a lump sum of $4.9 billion in cash. The deal bolsters Gilead's portfolio of oncology drugs through Forty Seven Inc.'s blood cancer medicine, which is expected to be on the market within 2 years.

The Defense Advanced Research Projects Agency wants warfighters to better trust artificial intelligence, so it's putting it to work in dogfights. DARPA's Air Combat Evolution program, currently out with a presolicitation notice, is working to deploy AI in "within-visual-range maneuvering," more commonly known in the air combat space as dogfighting. The ultimate goal is to not only to get AI into the cockpit -- the Defense Department doesn't foresee dogfights being the fighting style of the future -- but to train warfighters to trust computers the way they trust other humans. DARPA is seeking proposals for AI that can be gradually implemented into high-tempo, highly dynamic environments through simulation and eventual deployment, according to the notice. The Air Combat Evolution program is a part of a broader push for AI working alongside warfighters called Mosaic Warfare.

AI investment funds are not all about mega-caps like Alphabet, but about companies in other sectors using AI to increase competition, finds Fiona Rintoul. If you were to ask artificial intelligence (AI) how to save the planet, it would probably tell you to kill all humans, because they cause the pollution that creates climate change. But that would not be the response you wanted; therefore, you must recalibrate. "We need to ask the right questions of AI," says Rani Piputri, head of automated intelligence investing at NN Investment Partners. "In this situation, AI will nudge people to have fewer children."

Ilker is Co-founder of Botanalytics, conversational analytics company based in San Francisco.

I introduced the basic analogy, concept, and ideas behind "How GANs work". This post will do a little bit of a deep dive. Generative Adversarial Networks are a class of algorithms used in the unsupervised learning environment. As the name suggests they are called Adversarial Networks because they are is made up of two competing neural networks. Both networks compete with each other to achieve a zero-sum game. Both neural networks are assigned different job role i.e. contesting with each other. The process in GANs involves automatically learning to discover the regularities or patterns in input data.

Brain-machine interfaces have come a long way in the last decade. Now a group of computer scientists and neuroscientists from the University of San Francisco have taken things a step further. The researchers used two neural networks to decode their participant's brain activity and determine what they were saying. What they achieved was real-time translation of neural activity to text. There were four participants, each with 250 electrodes previously implanted in their brains to monitor epileptic seizures, over a region known as the perisylvian cortex.