Artificial intelligence startup Domino Data Lab Inc. said Tuesday it raised $100 million in new funding amid increased business interest in tools that help data scientists build and deploy AI applications. The funding will be used to scale its sales organization and build out its machine-learning platform's features and functions, said Nick Elprin, Domino Data's chief executive and one of its co-founders. Domino Data has raised $228 million since its founding in 2013. Private-equity firm Great Hill Partners led the series F round with participation from graphics-chip maker Nvidia Corp. and existing investors Coatue Management, Highland Capital Partners and Sequoia Capital. The company didn't disclose its valuation.

Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena. Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning. But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills.

A team of researchers from the RIKEN Center for Advanced Intelligence Project (Saitama, Japan) has developed a machine-learning platform capable of identifying features associated with prostate cancer recurrence in pathology images that were previously unknown to clinicians. In combination with pathologist-developed criteria, the technology may allow for more accurate cancer recurrence predictions. Conventionally, when clinicians and/or researchers train artificial intelligence (AI) systems, the technology is only able to learn and make predictions based on the information that has been inputted – there is no scope for the system to learn outside of what is currently known. In this study, no medical knowledge was inputted into the platform, rather, investigators employed'unsupervised' deep neural networks, called autoencoders, and utilized a subset of 13,188 non-annotated, whole-mount, diagnostic prostate pathology slide images from the Nippon Medical School Hospital (Tokyo, Japan), allowing the AI system to learn and make predictions independently. The team developed a method for translating the features identified by the machine-learning platform into high-resolution images that could be understood by clinicians.

When you want to see whether one CPU is faster than another, you have PassMark. But what do you do when you need to figure out how fast your machine-learning platform is--or how fast a machine-learning platform you're thinking of investing in is? Machine-learning expert David Kanter, along with scientists and engineers from organizations such as Google, Intel, and Microsoft, aims to answer that question with MLPerf, a machine-learning benchmark suite. Measuring the speed of machine-learning platforms is a problem that becomes more complex the longer you examine it, since both problem sets and architectures vary widely across the field of machine learning--and in addition to performance, the inference side of MLPerf must also measure accuracy. If you don't work with machine learning directly, it's easy to get confused about the terms. The first thing you must understand is that neural networks aren't really programmed at all: they're given a (hopefully) large set of related data and turned loose upon it to find patterns.

In 2016, venture capitalists invested $5 billion in startups involving artificial intelligence, representing a 40 percent increase from 2012. With hopes of securing a foothold in what promises to be a multi-billion dollar industry, some of the most influential companies in the world--including IBM, Apple, and Google--are pouring hundreds of millions of dollars into their AI research-and-development labs. Health care in particular has been a favourite target for these investments. Google's research website states that "machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people." As with any burgeoning industry, there have been gaffes along the way.

AI will continue to uncover data for all of the organizations that add machine-learning platforms. There have been many "ages" throughout human history, most notably the industrial age and the digital age. Now, we have officially entered the age of artificial intelligence (AI). Within this AI age are many technologies, including machine learning and deep learning. These are fundamentally transforming and altering the business landscape.

The machine-learning smarts that help Google know what's in a photo and let Amazon's Alexa carry on a conversation are getting a real job. "ML" platforms from vendors like Amazon, Google, IBM, Microsoft, and others can automate business processes on a previously impossible scale and free up employees for more creative, thought-intensive work. They also require a lot more commitment and, sometimes, coaxing than parking an Amazon Echo on a kitchen table or tapping a button to have Google back up the photos on your phone. But the payoff can also be correspondingly greater. "Every business process is just badly written software," observed Markus Noga, head of machine learning at SAP.

If you're a female entrepreneur looking for resources, say Hello Alice. Launched by Dell and Circular Board, Hello Alice is a machine-learning platform designed for female entrepreneurs. The platform has a friendly, how-can-I-help-you approach, and offers information on strategy, financing, technology and other topics. It also connects women to mentors, experts and events. As more women use the platform, it will get smarter, eventually predicting a user's needs and offering targeted content based on a startup's size, location, industry and other factors.

Alibaba Cloud, the cloud-computing arm of Alibaba Group, is making artificial intelligence (AI) technology more accessible to businesses and organizations with the debut of an upgraded machine-learning platform. Called PAI 2.0, the platform, which launched in 2015, will "help customers easily deploy large-scale data mining and modeling," Alibaba Cloud said in a statement. Machine learning is a branch of AI that gives computers the ability to absorb information, discern patterns in data and adapt to new input without explicit programming. China's first publicly available machine-learning platform, PAI is a suite of tools and AI software algorithms that allows businesses without an AI background to make practical use of Alibaba Cloud's ET program, an "artificial brain" that the company is using to solve complex problems such as predicting the winner of a popular Chinese reality TV show and helping to ease traffic congestion in the city of Hangzhou, China. "In the past year, Alibaba Cloud has implemented a number of real-life AI applications for customers across industries," said Alibaba Cloud Chief Scientist Dr. Jingren Zhou.

Israeli pharmaceutical firm Teva is partnering with US chipmaker Intel to develop wearables and a machine-learning platform that can be used in the treatment of Huntington's Disease. Huntington's is a genetic and typically fatal neurodegenerative disease that causes nerve cells in the brain to deteriorate. This leads to a host of behavioral and psychological problems, including involuntary writhing movements called chorea. There is no cure for Huntington's, but Teva and Intel hope to spur the development of next-generation treatment options by better understanding the progression of the disease and how current treatments impact a patients' quality of life. The companies plan to accomplish this by combining Intel's capabilities in analytics and algorithms with Teva's work around Huntington's treatment and research.