Formula 1 plans to use cloud technology and machine learning to deliver more engaging statistic and even predictions to fans watching races on television and on its digital platforms. Cloud giant Amazon Web Services (AWS) has been signed up as an official technology partner, with its technology used to crunch the data and deliver it in a more meaningful way to fans and commentators. Each Formula 1 car produces huge amounts of data that the teams use to optimise their strategies and it is this database that Liberty Media believes can be turned into something valuable for the audience. After all, this is a sport that claims to have been'doing' big data since before the term was coined. Data scientists are using 65 years' worth of historical race data to train deep learning models that can make race predictions and give fans an insight into why a team has adopted a particular strategy.

The field of deep learning is still in flux, but some things have started to settle out. In particular, experts recognize that neural nets can get a lot of computation done with little energy if a chip approximates an answer using low-precision math. But some tasks, especially training a neural net to do something, still need precision. IBM recently revealed its newest solution, still a prototype, at the IEEE VLSI Symposia: a chip that does both equally well. The disconnect between the needs of training a neural net and having that net execute its function, called inference, has been one of the big challenges for those designing chips that accelerate AI functions.

Time Series Forecasting is a key area that can lead to Return On Investment (ROI) in a business. Think about this: A 10% improvement in forecast accuracy can save an organization millions of dollars.

Facebook is buying London-based artificial intelligence start-up Bloomsbury, in yet another indication that the capital has become a hotbed for AI talent, ripe for cash-rich US tech giant's picking. Bloomsbury AI is a data analytics company that automates customer care and advice. It has proprietary algorithms that answer questions after trawling documents, which will prove a useful tool for Facebook's battle against fake news. The Silicon Valley tech giant will pay between $20m and $30m (£15m-£22m) to acquire the company in both cash and stocks, a move first reported by TechCrunch and confirmed by the Telegraph. It follows Google's purchase of Deepmind for £400m, another university-spin off...

The flagman waves the chequered flag as Red Bull's Dutch driver Max Verstappen crosses the finish of the Austrian Formula One Grand Prix in Spielberg, central Austria, on July 1, 2018. Formula 1 plans to use cloud technology and machine learning to deliver more engaging statistic and even predictions to fans watching races on television and on its digital platforms. Cloud giant Amazon Web Services (AWS) has been signed up as an official technology partner, with its technology used to crunch the data and deliver it in a more meaningful way to fans and commentators. Each Formula 1 car produces huge amounts of data that the teams use to optimise their strategies and it is this database that Liberty Media believes can be turned into something valuable for the audience. After all, this is a sport that claims to have been'doing' big data since before the term was coined. Data scientists are using 65 years' worth of historical race data to train deep learning models that can make race predictions and give fans an insight into why a team has adopted a particular strategy.

Currently, we stand on the brink of a fourth Industrial revolution. Artificial Intelligence or AI is the intelligence demonstrated by machines for performing tasks. It is a specialized section of computer science which focuses on creating intelligent machines that think, react and work like humans. Some of the activities computers with artificial intelligence are built for includes problem solving, learning, analysing, speech recognition, and much more. AI is a vast field in itself, and it encompasses a wide spectrum of technologies such as Machine Learning, Automated Intelligence System, Deep learning, Neural Network, Computational Argumentation, and Multi-agent Systems to solve problems that currently seem impossible.

Netradyne, a leader in Artificial Intelligence (AI) technology focusing on driver and commercial fleet safety, today announced that its Driveri platform has been selected as the winner of the "Best AI-based Solution for Transportation" award from AI Breakthrough, an independent organization that recognizes the top companies, technologies and products in the global Artificial Intelligence (AI) market today. "Netradyne's recognition by the Artificial Intelligence Awards as being the best AI-based solution for transportation reiterates our company's belief that we are driving forward the potential for AI in all aspects of the transportation space including commercial trucking and autonomous vehicles," said Sandeep Pandya, Netradyne President. "This organization recognizes cutting edge AI technology at the highest levels and to have the Driveri platform mentioned alongside so many other impactful companies such as Google, NVIDIA and IBM is a tremendous honor. We are elated by the recognition and will continue to look for innovative ways to showcase AI's unique and impactful capabilities within transportation." The mission of the AI Breakthrough Awards is to honor excellence and recognize the innovation, hard work and success in a range of AI and machine learning related categories, including AI platforms, Deep Learning, Smart Robotics, Business Intelligence, Natural Language Processing, industry specific AI applications and many more.

Half the year has flown by and that brings us to the June edition of our popular series – the top GitHub repositories and Reddit threads from last month. During the course of writing these articles, I have learned so much about machine learning from either open source codes or invaluable discussions among the top data science brains in the world. What makes GitHub special is not just it's code hosting and social collaboration features for data scientists. It has lowered the entry barrier into the open source world and has played a MASSIVE role in spreading knowledge and expanding the machine learning community. We saw some amazing open source code being released in June.

Machine learning solutions in the real world are rarely just a matter of building and testing models. Managing and automating the lifecycle of machine learning models from training to optimization is, by far, the hardest problem to solve in machine learning solutions. To control the lifecycle of a model, data scientists need to be able to persist and query its state at scale. This problem might seem trivial until you consider that any average deep learning model can include hundreds of hidden layers and millions of interconnected nodes;) Storing and accessing large computation graphs is far from trivial. Most of the times, data science teams spend a lot of time trying to adapt commodity NOSQL databases to machine learning models before arriving to the not-so-obvious conclusion: Machine learning solutions need a new type of database.

WHEN TAY MADE HER DEBUT in March 2016, Microsoft had high hopes for the artificial intelligence–powered "social chatbot." Like the automated, text-based chat programs that many people had already encountered on e-commerce sites and in customer service conversations, Tay could answer written questions; by doing so on Twitter and other social media, she could engage with the masses. But rather than simply doling out facts, Tay was engineered to converse in a more sophisticated way--one that had an emotional dimension. She would be able to show a sense of humor, to banter with people like a friend. Her creators had even engineered her to talk like a wisecracking teenage girl. When Twitter users asked Tay who her parents were, she might respond, "Oh a team of scientists in a Microsoft lab. They're what u would call my parents." If someone asked her how her day had been, she could quip, "omg totes exhausted."