You already know what all of your friends are eating, so you might as well know how to make it, too. You already know what all of your friends are eating, so you might as well know how to make it, too. When someone posts a photo of food on social media, do you get cranky? Is it because you just don't care what other people are eating? Or is it because they're enjoying an herb-and-garlic crusted halibut at a seaside restaurant while you sit at your computer with a slice of two-day-old pizza?

OpenText has launched an artificial intelligence platform built on top of OpenText Analytics and Apache Spark. OpenText Magellan uses machine learning to help enterprises better deal with massive quantities of data, both structured and unstructured. Magellan integrates visualization, voice, video, search, text, natural language processing, and semantic and numeric engines, OpenText's Mark Barrenechea wrote in a blog entry last year. "It is an open platform built on open standards like HTML5, Java, SQL, and Hadoop," Barrenechea wrote. "Developers will be able to build on this platform to expand its rich ecosystem. Because it is built on open standards, Magellan is embeddable, offering our customers access to a wealth of well-studied and well-written algorithms."

Machines are learning to process simple commands by exploring 3-D virtual worlds. Devices like Amazon's Alexa and Google Home have brought voice-controlled technology into the mainstream, but these still only deal with simple commands. Making machines smart enough to handle a real conversation remains a very tough challenge. And it may be difficult to achieve without some grounding in the way the physical world works. Attempts to solve this problem by hard-coding relationships between words and objects and actions requires endless rules, making a machine unable to adapt to new situations.

Using the previously developed concepts of semantic spacetime, I explore the interpretation of knowledge representations, and their structure, as a semantic system, within the framework of promise theory. By assigning interpretations to phenomena, from observers to observed, we may approach a simple description of knowledge-based functional systems, with direct practical utility. The focus is especially on the interpretation of concepts, associative knowledge, and context awareness. The inference seems to be that most if not all of these concepts emerge from purely semantic spacetime properties, which opens the possibility for a more generalized understanding of what constitutes a learning, or even `intelligent' system. Some key principles emerge for effective knowledge representation: 1) separation of spacetime scales, 2) the recurrence of four irreducible types of association, by which intent propagates: aggregation, causation, cooperation, and similarity, 3) the need for discrimination of identities (discrete), which is assisted by distinguishing timeline simultaneity from sequential events, and 4) the ability to learn (memory). It is at least plausible that emergent knowledge abstraction capabilities have their origin in basic spacetime structures. These notes present a unified view of mostly well-known results; they allow us to see information models, knowledge representations, machine learning, and semantic networking (transport and information base) in a common framework. The notion of `smart spaces' thus encompasses artificial systems as well as living systems, across many different scales, e.g. smart cities and organizations.

Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go. Sure, A.I. systems have mastered an array of games, from chess and Go to "Jeopardy" and poker, but the technology continues to struggle in the real world. Robots fall over while opening doors, prototype driverless cars frequently need human intervention, and nobody has yet designed a machine that can read reliably at the level of a sixth grader, let alone a college student. Computers that can educate themselves -- a mark of true intelligence -- remain a dream. Even the trendy technique of "deep learning," which uses artificial neural networks to discern complex statistical correlations in huge amounts of data, often comes up short.

Opaque and potentially biased mathematical models are remaking our lives--and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers, together with the American Civil Liberties Union, launched an effort to identify and highlight algorithmic bias. The AI Now initiative was announced at an event held at MIT to discuss what many experts see as a growing challenge. Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities.

New research examining how organisations are evolving from a traditional office environment to a digital workplace, has revealed that gaining competitive advantage and improving business process are among the top goals of their digital transformation strategy. This is according to 40% of 800 organisations in 15 countries on five continents that were interviewed for Dimension Data's survey. The report found that digital transformation is not just about adopting the technologies of the past: 62% of research participants expect to have technology such as virtual advisors in their organisations within the next two years. In addition, 58% expect to start actively investing in technology that powers virtual advisors in the next two years. Today, the digital workplace is no longer just made up of managers and those managed; co-workers collaborating with one another to complete projects; and employees interacting with customers and partners.

Does Siri have trouble with your accent? A project is turning to crowdsourced voice donations to overcome this problem, and iron out some of the other inherent problems with voice recognition. Voice assistants like Siri and Alexa are trained on huge databases of recorded speech. But if those don't contain enough samples of a particular accent or dialect, the voice assistants will struggle to understand people who speak that way. So Mozilla – the foundation behind the Firefox web browser – is turning to crowdsourcing to create voice recognition systems that avoid these problems.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.

By now our future is clear: We are to be cared for, entertained, and monetized by artificial intelligence. Existing industries like healthcare and manufacturing will become much more efficient; new ones like augmented reality goggles and robot taxis will become possible. But as the tech industry busies itself with building out this brave new artificially intelligent, and profit boosting, world, it's hitting a speed bump: Computers aren't powerful and efficient enough at the specific kind of math needed. While most attention to the AI boom is understandably focused on the latest exploits of algorithms beating humans at poker or piloting juggernauts, there's a less obvious scramble going on to build a new breed of computer chip needed to power our AI future. One datapoint that shows how great that need is: software companies Google and Microsoft have become entangled in the messy task of creating their own chips.