IBM has been researching, developing and investing in AI technology for more than 50 years. The public became aware of a major advance in 2011, when IBM Watson won the historic Jeopardy! Since that time, the company has advanced and scaled the Watson platform, and applied it to various industries, including healthcare, finance, commerce, education, security, and the Internet of Things. We are deeply committed to this technology, and believe strongly in its potential to benefit society, as well as transform our personal and professional lives. To this end, we have engaged thousands of scientists and engineers from IBM Research and Development, and partnered with our clients, academics, external experts, and even our competitors to explore all topics around AI.

Starting this week, Uber users in Pittsburgh will be able to get rides in the company's new self-driving cars. Those entering one of Uber's autonomous vehicles will easily notice a few differences. Most obvious is the large laser scanner that sits on top of the car and spins really fast. In addition to the main scanner, 64 more laser beams will sweep the area surrounding the car as it drives to detect and measure the distance of objects around it. Other nifty sensors and machine learning algorithms will help the car build accurate three-dimensional maps of the street, while riders get to view how far they've traveled and how the car is doing on an iPad screen in the backseat.

As the term "machine learning" has heated up, interest in "robotics" (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics? While only a portion of recent developments in robotics can be credited to developments and uses of machine learning, I've aimed to collect some of the more prominent applications together in this article, along with links and references. Before I delve into machine learning in robotics, go ahead and define "robot". Though at first this might seem simple, it's no easy task to come to an agreement on just what a robot is and what it is not, even amongst roboticists.

Perhaps the biggest moment so far in the machine learning field was when Google's DeepMind AI beat Lee Sedol, an 18-time world Go champion. Go is a highly complex game even for humans, and machines weren't supposed to start winning against professionals for at least another decade. DeepMind winning against a Go world champion sent shockwaves through the technology industry.

The Defense Advance Research Projects Agency will fund the development of sensors and artificial intelligence systems that could help break into, extract, and analyze information from enemy devices and communication systems. The components and systems will arm the U.S. with more data to analyze enemy moves and strategy. Information is king in wars, and DARPA wants to develop technology that can break into enemy systems. "They are talking about going into any situation and extracting information at any time, [with] artificial intelligence systems that can attack and hack any network," said Jim McGregor, principal analyst at Tirias Research. DARPA wants to fund the development of sensors and electromagnetic systems that could break into point-to-point wired and wireless communications, even ones that are not linked to the internet.

Researchers at Brighton and Sussex Medical School (BSMS) and the University of Sussex, will use techniques developed to catalogue galaxies to analyse GP data in a search for early indicators of the medical condition. Dr Philip Rooney, a research fellow in astrophysics at Sussex, who will conduct much of the data analysis, said: "We regularly apply statistical and machine-learning techniques to catalogues of galaxies and galaxy spectra. "Researchers can apply these techniques to GP patient records, where the clinical condition is analogous to a galaxy, the GP database is similar to a galaxy catalogue and diagnosis of a condition is similar to the modelling of galaxy properties." Timely diagnosis of dementia allows patients to maximise their quality of life, benefit from treatments and plan for the future.

Researchers at Brighton and Sussex Medical School (BSMS) and the University of Sussex, will use techniques developed to catalogue galaxies to analyse GP data in a search for early indicators of the medical condition. The Astrodem study, which will look at 96,000 sets of records, has been awarded 94,000 by the Wellcome Trust. It will look into data including the number of appointments a patient has had, or whether they attended with a family member, and combine it with other clinical information known to predict dementia. The aim is to provide a wide range of indicators that may help GPs identify their patients at high risk of developing it. Project lead Dr Elizabeth Ford said: "Dementia is one of the greatest public health challenges of our era and more timely diagnosis may help many patients. "GPs could benefit from a'real-time' risk stratification tool to use in their workflow, which could then be used sensitively to bring up the topic of memory problems and perhaps start a conversation about a referral for a memory assessment, if they feel it is appropriate." Dr Philip Rooney, a research fellow in astrophysics at Sussex, who will conduct much of the data analysis, said: "We regularly apply statistical and machine-learning techniques to catalogues of galaxies and galaxy spectra.

Artificial intelligence (AI) has already transformed our lives -- from the autonomous cars on the roads to the robotic vacuums and smart thermostats in our homes. Over the next 15 years, AI technologies will continue to make inroads in nearly every area of our lives, from education to entertainment, health care to security. The question is, are we ready? Do we have the answers to the legal and ethical quandaries that will certainly arise from the increasing integration of AI into our daily lives? Are we even asking the right questions? Now, a panel of academics and industry thinkers has looked ahead to 2030 to forecast how advances in AI might affect life in a typical North American city and spark discussion about how to ensure the safe, fair, and beneficial development of these rapidly developing technologies.

Despite the benefit of a magnifying glass, radiologists analyzing mammograms still recommend biopsies unnecessarily 20 percent of the time. Software developed by Houston Methodist artificial intelligence researchers promises quicker, more accurate results.

With the backing of the White House, Seattle cancer researchers are enlisting artificial intelligence experts across the globe to catch the early signs of breast cancer. The Digital Mammography DREAM Challenge – a partnership of Seattle Cancer Care Alliance, Group Health Research Institute, Fred Hutchinson Cancer Research Center, Sage Bionetworks and others – was announced at Vice President Joe Biden's Cancer Moonshot Summit that in June brought together health care providers, research institutions and technology experts for a national day of action. For the challenge, more than 300 tech experts at machine and deep learning labs around the world will get data on 86,000 breast cancer patients, including more than 640,000 digital mammography images, and use their expertise to develop data models that can better predict the presence of breast cancer.