It's a brand new year, and by most reliable indicators – the latest demos at CES 2017, the buzz on all the tech blogs and even the pre-roll ads interrupting my binge watching of Crazy Ex-Girlfriend – it looks like 2017 will be the year that voice interaction reaches mainstream adoption. Voice interaction – the ability to speak to your devices, and have them understand and act upon whatever you're asking them – was everywhere this year. Device manufacturers of all shapes and sizes heavily integrated voice capabilities into their offerings at CES 2017, with Amazon's Alexa stealing the show as their AI platform of choice. The rapid proliferation of voice interaction capabilities in our individual digital ecosystems raises critical questions for any designer whose work plays a role in the customer experience. It's becoming clear that voice interaction will soon become an expected offering as either an alternative, or even a full replacement to, traditional visual interfaces.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

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More than 30,000 branded chatbots and 6,000 voice-activated skills hit the market during 2016. Now, chatbots and peer-to-peer messaging apps will move beyond last year's market-entry issues and into mainstream use. For mobile messaging, 2017 is poised to be the Year of the Customer. This year, every Fortune 1000 business will add a chatbot to their tech and marketing stack, and consumers will finally begin to understand the true potential of chatbots. Previously, chatbots have faced issues with functionality, usability, adoption, discoverability, and monetization; however, this year, messaging platforms will make their products so valuable they can't be ignored.

Accenture thinks that the future of technology is centered around people. It believes that human needs will be the common thread for the rapid advance of technologies such as artificial intelligence. The technology global consulting firm is releasing its annual forecast report, dubbed Technology for People, as part of its annual predictions today. The report states that we are beginning to see the emergence of technology for people, by people -- technology that seamlessly anticipates our needs and delivers hyperpersonalized experiences. "The pace of technology change is breathtaking, bringing about the biggest advancements since the dawn of the Information Age," said Paul Daugherty, Accenture's chief technology and innovation officer, in a statement.

Organizations have made large investments in big data platforms, but many are struggling to realize business value. While most have anecdotal stories of insights that drive value, most still rely only upon storage cost savings when assessing platform benefits. At the same time, most organizations have treated machine learning and other cognitive technologies as "science projects" that don't support key processes and don't deliver substantial value. However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. They're employing a new generation of software, skills, and infrastructure technologies to solve complex, detailed problems and deliver substantial business value. One company found the approach so successful that a manager said it was like "printing money"--a reliable, production-based approach to generating revenue.

While this site is dedicated to listing events that are specifically about Artificial Intelligence and Machine Learning we recognise that there are a number of other events held, that while they are not purely about AI, they will be covering the topic as part of the event. In such cases, we are very happy to list those events here in our related events page. If you know of an event that is related to Artificial Intelligence, Machine Learning or Neuroscience and is not listed here, please let us know and we will get it listed so others can be informed about it.

It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving. A dermatologist uses a dermatoscope, a type of handheld microscope, to look at skin. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists.

Intel and Chinese telecoms company ZTE claim to have achieved a new record – more than 1000 images per second in facial recognition – with what is known as'theoretical high accuracy' achieved for their custom topology. "Perception, such as recognising a face in an image, is one of the essential goals of the ZTE 5G system," said Duan Xiangyang, vice president of the ZTE Wireless Institute. "Deep learning technology is important as it can enable such perception in mobile edge computing systems, thus making ZTE's 5G system smarter." The test took place in Nanjing, where ZTE's engineers used Intel's Arria 10 FPGA for a cloud inferencing application using a convolutional neural networks (CNN) algorithm. According to the company, the deep learning designs can be migrated from the Arria 10 FPGA family to the Intel Stratix 10 FPGA family, and users can expect up to nine times performance boost.

Artificial intelligence can boost business productivity and profitability -- as well as find ways to foster human happiness, according to the director of Hitachi's artificial intelligence laboratory. Interest in AI traditionally has centered on purpose-built technology crafted for a particular reason, such as Google's AI program that last year beat a champion player at the Chinese strategy game Go, said Kazuo Yano during a 14 December presentation at AAAS headquarters. The address was part of the Hitachi lecture series, which has brought speakers to AAAS for nearly a decade to examine a wide range of issues related to science and society. Yano, who serves as Hitachi's chief corporate scientist, noted that AI increasingly is being used to address the needs of business. To cope with changing variables like customer behavior and marketplace position, businesses now can take advantage of the flexibility offered by "general-purpose AI," which can be added to existing systems, he said.