Jason Mars is an African-American professor of computer science who also runs a tech start-up. When his company's artificially intelligent smartphone app talks, he said, it sounds "like a helpful, young Caucasian female." "There's a kind of pressure to conform to the prejudices of the world" when you are trying to make a consumer hit, he said. "It would be interesting to have a black guy talk, but we don't want to create friction, either. First we need to sell products." Mr. Mars's start-up is part of a growing high-tech field called conversational computing.

The theory of computability was launched in the 1930s, by a group of logicians who proposed new characterizations of the ancient idea of an algorithmic process. The most prominent of these iconoclasts were Kurt Gödel, Alonzo Church, and Alan Turing. The theoretical and philosophical work that they carried out in the 1930s laid the foundations for the computer revolution, and this revolution in turn fueled the fantastic expansion of scientific knowledge in the late 20th and early 21st centuries. Thanks in large part to these groundbreaking logico-mathematical investigations, unimagined number-crunching power was soon boosting all fields of scientific enquiry. The motivation of these three revolutionary thinkers was not to pioneer the disciplines now known as theoretical and applied computer science, although with hindsight this is indeed what they did.

Analyses performed using Spark of brain activity in a larval zebrafish: embedding dynamics of whole-brain activity into lower-dimensional trajectories. The growth of data volumes in industry and research poses tremendous opportunities, as well as tremendous computational challenges. As data sizes have outpaced the capabilities of single machines, users have needed new systems to scale out computations to multiple nodes. As a result, there has been an explosion of new cluster programming models targeting diverse computing workloads.1,4,7,10 At first, these models were relatively specialized, with new models developed for new workloads; for example, MapReduce4 supported batch processing, but Google also developed Dremel13 for interactive SQL queries and Pregel11 for iterative graph algorithms. In the open source Apache Hadoop stack, systems like Storm1 and Impala9 are also specialized. Even in the relational database world, the trend has been to move away from "one-size-fits-all" systems.18 Unfortunately, most big data applications need to combine many different processing types. The very nature of "big data" is that it is diverse and messy; a typical pipeline will need MapReduce-like code for data loading, SQL-like queries, and iterative machine learning. Specialized engines can thus create both complexity and inefficiency; users must stitch together disparate systems, and some applications simply cannot be expressed efficiently in any engine. In 2009, our group at the University of California, Berkeley, started the Apache Spark project to design a unified engine for distributed data processing. Spark has a programming model similar to MapReduce but extends it with a data-sharing abstraction called "Resilient Distributed Datasets," or RDDs.25 Using this simple extension, Spark can capture a wide range of processing workloads that previously needed separate engines, including SQL, streaming, machine learning, and graph processing2,26,6 (see Figure 1).

Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. Our research shows that the next generation of neural nets is most likely to be led by Spiking Neural Nets (SNNs) that are a return to the'strong' AI tradition and closely mimic actual brain function. Unlike CNNs that fire signals to every one of their deep layer connections every time, SNNs are modeled after the fact that in the brain neurons do not constantly communicate with one another. Rather they communicate in spikes of signals or more correctly short trains of spiking signals. As each spike in the train arrives at a neuron it raises the potential of that neuron until finally a spike arrives that tips it over its potential threshold and it in turn fires, propelling the signal onward.

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A few months back I was making my way through the latest literature on "real time analytics" and "in stream analytics" and my blood pressure was rising. The cause was the developer-driven hyperbole that claimed that the creation of brand new insights using advanced analytics has become "real time". The issue then as now is the failure to differentiate between time-to-action and time-to-insight. Not infrequently the statements about'fast data' are accompanied by a diagram like this, which to me has a fatal flaw. The flaw, to my way of thinking, is that there are really two completely different tasks here with very different time frames.

When Bob Dylan won the 2016 Nobel Prize in Literature, it shocked the humanitarian world. What's more, Dylan himself hasn't behaved like a traditional Nobel winner: he hasn't commented on the honor and has yet to give an acceptance speech. At least one member of the Nobel panel has called Dylan's silence "rude and arrogant," and the public has been reminded that if he doesn't give a lecture within six months, he won't receive the $900,000 prize money. Selecting Dylan as a Nobel laureate may be contentious, but it's mostly a sign of growth for intellectual society -- at least in Literature, no one is off-limits, not even mumbling masters of wordplay and songwriting. Growing pains are expected as the world of mainstream politics, activism and academia is suddenly forced to consider the potential of new industries, and vice versa.

Machine learning developers who want to use Google's Go language as their development platform have a small but growing number of projects to choose from. Rather than call out to libraries written in other languages, chiefly C/C, developers can work with machine learning libraries written directly in Go. Existing machine learning libraries in other languages have a far larger culture of users, but there's clearly an interest in having Go toolkits that take advantage of the language's conveniences. GoLearn, described as a "batteries included" machine learning library, is one of the most prominent. "Simplicity, paired with customisability, is the goal," the developers write in their introduction to the project.

Spot the Milky Way! Stunning image shows what the universe would look like if human eyes could see radio waves The center of the Milky way is pictured in stunning radio colour, just one of more than 300,000 galaxies observed in one of the biggest radio sky surveys ever assembled. Red indicates the lowest frequencies, green the middle frequencies and blue the highest frequencies. The Gleam survey observes radio waves traveling through space at frequencies of 70-230 HMz. Pictured is a'radio colour' view of the sky above a portion of the Murchison Widefield Array radio telescope, located in outback Western Australia. The views expressed in the contents above are those of our users and do not necessarily reflect the views of MailOnline.

Next month, Dr. Iris Pear will present her groundbreaking new study at the International Conference on Atomic and Nuclear Physics. Iris Pear – a play on "Siri Apple" – is the invention of Christophe Bartneck, an associate professor of computer science at New Zealand's University of Canterbury. The study in question is completely nonsensical, procedurally generated by iOS's autocomplete function. Why, then, did a conference for "leading academic scientists" select it for presentation? On Thursday, Dr. Bartneck received an invitation to submit research for an upcoming conference on nuclear physics.