At present, predictive analytics is the most used form of AI in enterprise and companies are focusing on innovation, patenting their AI developments at a faster rate than ever before. Join us as we explore the rise of artificial intelligence in six charts including the top investors in AI and the most used AI enterprise solutions. As of June 2016, artificial intelligence received $974m of funding. This year's funding is set to surpass 2015's total and CB Insights suggests that 200 AI-focused companies have raised nearly $1.5 billion in equity funding. AI isn't limited to the business sphere, in fact the personal robot market, including'care-bots', could reach $17.4bn by 2020.

The two companies will leverage their expertise and advantages in online and offline services to cooperate on projects in mobile Internet, AI, big data and telecom services. Baidu will help China Unicom put the services of more than 10,000 brick-and-mortar outlets and 300,000 franchised stores online. China Unicom will offer Baidu stronger telecom infrastructure support such as Internet data centers and information and communication technology. Robin Li, Baidu board chairman and CEO, said his company already cooperates closely in linking mobile search, mapping and group buying services with China Unicom's services, and Baidu is looking forward to impressive results from AI technology. Wang Xiaochu, board chairman of China Unicom, called the move an important step for cross-sector cooperation between Internet companies and telecom firms in line with the government's "Internet Plus" proposal to upgrade traditional sectors with IT technology.

Our company Evertracker offers a smart and self-learning technology platform. Our clients use our solution to make their supply chains fully transparent, to predict the future and to automate processes. We've developed an IoT and Artificial Intelligence platform. We constantly develop our technology further to make our system smarter. They increase the quality of their network and predict events within their logistics chain.

Big data analytics can transform how businesses operate. While marketing and sales businesses cottoned on to this early, more and more previously non-tech-focused companies are realising the benefit of having expertise on board. "The way you can use data is a way not just to understand customers and products better, but also be organised in a way that makes decision-making faster and allows people to have more autonomy," says Anthony Fletcher, chief executive of snack company Graze. Graze has its own dedicated data team, who "democratise data". Mr Fletcher says: "This involves finding sources of data, cleaning and feeding it into our central cloud-based data warehouse where anyone can access it anywhere, on a variety of devices. "This tracks literally thousands of different data sources from how fast our factory lines are running, the strategies for posting boxes in the US, 15,000 pieces of product feedback we get an hour, social data, trends in sales, how people are using our mobile site, and so on." The data scientists at Graze are also skilled at doing pieces of complex data analysis from attribution, A/B tests, machine-learning or various types of regression. "This often involves not only maths, but being really good at visualising data in ways which really tell the story," says Mr Fletcher. At Graze it reports to the chief executive. "This was highly unusual, but reflected the efforts of building the original infrastructure and creating the right culture around data," says Mr Fletcher. "The important thing today is they are a team who work across the business.

Artificial Lawyer caught up recently with Dan Rubins, the Founder and CEO – and also the CTO – of Legal Robot, one of the new breed of AI-driven document review start-ups. We discussed how he moved from working in a medical services company to joining the fray as a legal AI pioneer, how working on smart contracts may be where the company eventually ends up and why there's a big world of opportunity out there. The San Francisco-based founder of Legal Robot, Dan Rubins, is not a lawyer by background. But, his experiences have taught him a lot about the inefficiencies of document review, while his long term interest in technology and programming has also helped. Perhaps, most fundamentally, Rubins is a self-proclaimed engineer.

As the result is largely independent of way the classification is learned, it has to be a property of the images. What it seems to be telling us is that the statistics of natural images is such that the decision boundaries - the surfaces in the high-dimensional data space - are such that there are a small number of directions in which a very small step takes you across the boundary. To put it another way, the distribution of images and classification boundaries is such that there are directions in which a small translation has a high probability of crossing the boundary.

Early A.I. product offerings are making it more efficient to plan meetings and provide customer service. Artificial intelligence (A.I.) has become one of the hottest topics in business recently, with developers building the technology into everything from cars to refrigerators. Some have expressed skepticism about AI's potential, but early product offerings have revealed technology that appears to actually enhance the work professionals do rather than replace it. Jamie Domenici, Salesforce's VP of Small Business, is excited about the way A.I. is affecting sales and marketing. I sat down with her at this year's Dreamforce in San Francisco, where attendees got a close up look at the company's new A.I. tool Einstein, which the company is embedding into its products and services.

Just because Halloween is over doesn't mean the fears have ended. A new project uses autonomous computers to discover your deepest nightmares. The project, dubbed "Nightmare Machine," comes from a partnership between Australia and the U.S. The algorithm would let a computer understand what makes certain videos or images scary. One would hope the algorithm would then remove the scary bits and replace them with something more appealing. However, it uses data to transform any photo into something terrifying.

The concept of machine learning has been around for some time. Deep learning is an area of research aimed at taking things further still and getting closer to an artificial intelligence system by using neural networks in a way that imitates the human brain. Sometimes also referred to as hierarchical learning or deep structured learning, it seeks to model data in order to solve problems like object and facial recognition, natural language processing and speech recognition. It's called deep learning because the data is processed through a number of layers, usually in a neural network, the output from one layer forming the input for the next. This allows for machines to learn unsupervised as high level features are derived from low level ones to create a hierarchical representation of the data.

Sparse coding is a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, that are optimal in the class of first-order methods for non-smooth, convex functions, such as the Iterative Soft Thresholding Algorithm and its accelerated version (ISTA, FISTA). However, these methods don't exploit the particular structure of the problem at hand nor the input data distribution. An acceleration using neural networks was proposed in Gregor & Lecun (2010), coined LISTA, which showed empirically that one could achieve high quality estimates with few iterations by modifying the parameters of the proximal splitting appropriately. In this paper we study the reasons for such acceleration.