Apple CEO Tim Cook says you won't have to give up your privacy to have a great AI assistant Tim Cook on A.I.: "I Don't Think We Have to Throw Our Privacy Away" Now AI is Deliberately Trying to Scare Us, if We Aren't Already TIM COOK: Here's why assistants on phones are better than home speakers like the Echo Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Apple CEO Tim Cook says you won't have to give up your privacy to have a great AI assistant Tim Cook on A.I.: "I Don't Think We Have to Throw Our Privacy Away" Now AI is Deliberately Trying to Scare Us, if We Aren't Already TIM COOK: Here's why assistants on phones are better than home speakers like the Echo Machine Learning Veterans Launch'Element AI' - A Montreal Based Artificial Intelligence Startup ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. AI'lawyer' correctly predicts outcomes of human rights trials AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

Data-driven algorithms govern many aspects of life: university admissions, resume screening, and a person's ability to get a car or home loan. Often, using data leads to more efficient allocation of resources and better outcomes for everyone. But algorithms can come with unintended consequences--and without care, their application can result in a society we don't want. Typically, we think of algorithms as being neutral and objective, but when software is written and trained by humans, it often encodes the biases and prejudices of the people that make and shape it. Ultimately, the biases built into algorithms can be racist and marginalize low-ranking socioeconomic groups.

An artificial intelligence (AI) judge has accurately predicted most verdicts of the European Court of Human Rights, and might soon be making important decisions about cases. Scientists built an artificial intelligence computer that was able to look at legal evidence as well as considering ethical questions to decide how a case should be decided. And it predicted those with 79 per cent accuracy, according to its creators. The algorithm looked at data sets made up 584 cases relating to torture and degrading treatment, fair trials and privacy. The computer was able to look through that information and make its own decision – which lined up with those made by Europe's most senior judges in almost every case.

In an earlier blog, "Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation", I introduced the need for Dynamical ML as we now enter the "Walk" stage of "Crawl-Walk-Run" evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two during supervised "Training" and use this static map for business purposes during "Operation". I made the following points using IoT as an example. Dynamical ML solution involves State-Space data model (more below). What more does a Dynamical ML solution offer?

Microsoft has released an updated version of Microsoft Cognitive Toolkit, a system for deep learning that is used to speed advances in areas such as speech and image recognition and search relevance on CPUs and NVIDIA GPUs. The toolkit, previously known as CNTK, was initially developed by computer scientists at Microsoft who wanted a tool to do their own research more quickly and effectively. It quickly moved beyond speech and morphed into an offering that customers including a leading international appliance maker and Microsoft's flagship product groups depend on for a wide variety of deep learning tasks. "We've taken it from a research tool to something that works in a production setting," said Frank Seide, a principal researcher at Microsoft Artificial Intelligence and Research and a key architect of Microsoft Cognitive Toolkit. The latest version of the toolkit, which is available on GitHub via an open source license, includes new functionality that lets developers use Python or C programming languages in working with the toolkit.

Udacity, the education platform focused on helping workers gain skills they need for great careers in tech, has partnered with IBM Watson, Didi Chuxing and Amazon Alexa to offer a new nanodegree in artificial intelligence, the companies announced today at the IBM World of Watson conference. IBM Watson is co-developing the curriculum of the course with Udacity. Chinese ride-hailing company Didi Chuxing intends to hire students who successfully complete the nanodegree, as does IBM. And Amazon Alexa is serving as an advisor to Udacity in developing the new AI nanodegree. According to Udacity's founder Sebastian Thrun, who previously started Google's innovation shop Google X and its self-driving car initiative, the new AI nanodegree will be for students who already have a level of mastery in software development.

Much of our current understanding about Artificial Intelligence is informed by what we see in popular culture. Due to ignorance and popular cultural portrayals of A.I. and machine learning, many people fear machines that are capable of computing complex tasks. There exists the belief that artificial intelligence could operate in a way that is counter to humanity's best interests, conjuring images of The Terminator and other similar films. As we explore the boundaries of the technology with consumer-facing technology like self-driving cars, and machine-learning algorithms such as recommendation engines, the general public is learning more and more about how these things work. But will we get to a point where people accept AI? A.I. refers to software that models its programming on human behaviour, mimicking rational and logical decision making processes.

AI can be a big boon to HR if we get past problems with data. Last week I attended the #Dreamforce conference, the annual conference of Salesforce.com, while in Chicago the #HRTech Conference was held. At #Dreamforce I watched demonstration of Salesforce.com's new Artificial Intelligence component called Einstein. As I sat their watching what Einstein could do for sales and marketing I was wondering if anyone has anything similar underway in HR. Last year I was at a IBM conference and learned about Watson, IBM's version of AI.