Darren Holly steers coils of steel through Pentaflex, a manufacturer of parts for heavy trucks, in Springfield, Ohio. Darren Holly steers coils of steel through Pentaflex, a manufacturer of parts for heavy trucks, in Springfield, Ohio. Americans who endured the brutal 2007-2009 recession and slow recovery now are seeing an economic sunrise: Wages are up, jobs are growing and more families are lifting themselves up out of poverty. And yet, dark clouds are still hanging over millions of Americans. No set of sunny statistics can help an unemployed coal miner in Kentucky pay the mortgage.

Quid, a data analytics firm, uses proprietary software to search, visualize and analyze text. Quid, a data analytics firm, uses proprietary software to search, visualize and analyze text. Predictions are for psychics -- and in this very unpredictable political season they might do a better job than the pundits. I set out to see how well it could predict which controversies around the candidates were likely to re-emerge over the course of a month. And two human pundits have agreed to compete against the machine.

Most of us have wondered what we might do if we didn't need to work – if we woke up one morning to discover we had won the lottery, say. We entertain ourselves with visions of multiple homes, trips around the world or the players we would sign after buying Arsenal. For many of us, the most tantalising aspect of such visions is the freedom it would bring: to do what one wants, when one wants and how one wants. But imagine how that vision might change if such freedom were extended to everyone. Some day, probably not in our lifetimes but perhaps not long after, machines will be able to do most of the tasks that people can.

Since blogging that I Need an AI BS-Meter a number of people have sent me pointers to a subset of AI I loosely think of as Result Explainers -- everything from pending government regulations (EU's Global Data Protection Regulations -- GDPR) to the latest in academic research (Local Interpretable Model-agnostic Explanations -- LIME). As the authors of the EU's GDPR state, widespread adoption of AI cannot occur until vendors are able to communicate results in a "concise, intelligible and easily accessible form, using clear and plain language." This got me thinking, "What should Result Explainers look like?" Should they generate trust scores, a series of Google-Maps like directions that get you from data to results, a series of diagrams? And as my colleague Patrick at Lab41 has pointed out, "Why should we trust a Result Explainer if we don't trust AI to begin with? As you might expect there isn't one right answer. That said, recent advances in recommenders, digital assistants, user interface design and initiatives like DARPA's recently announced Explainable Artificial Intelligence (XAI) grand challenge suggest we may be on the brink of a few breakthroughs. Again, as the authors of the EU's General Data Protection Regulations note, while the resulting classifiers, models, predictors, etc. can be very powerful they also frequently confound explanation -- e.g., the output of SVMs and Gaussian processes can be difficult to render, ensemble methods hide information as a result of aggregation and averaging, neural nets create high data dimensionality, and so on. End users care a lot more about results than they do about models. Unfortunately assessing result quality takes us right back to the models, as nonparametric models are only as good as the data used to train them (along with the type of model structure and associated parameters that were selected). But these models frequently hide information. Part of the magic of AI is that it finds stuff based on features that previously may not have been well understood. Unfortunately, the features models train on are frequently unclear. Assigning labels to pre-trained models can help mitigate some of this ambiguity -- e.g., "This model was trained with over 100,000 high-res color images of cats." These labels may be misleading though, as the model may contain feature biases that are not well understood -- e.g., "the training data is dominated by images of "well-fed, indoor cats from Japan."

In a recent article we discussed the topic of cybersecurity and gave you 10 publicly traded cyber security companies you could invest in to play this theme. As with any technology niche, some of the most exciting players are often startups because they are high risk and high reward. For retail investors, it becomes very difficult to invest in startups but nonetheless you should be aware of what they are up to because those publicly traded stocks you hold might just be displaced by a nimble startup. So how can we tell which cybersecurity startups are the hottest? The best way is to follow the money and look at what venture capital (VC) investors think is hot.

In the world which has ended my name was Dr. Adrian Bonner. I was civilian head of the quantum computer lab at the DOD, under contract from Nexus Robotics. I have decided to keep this journal as a record of my thoughts on what has happened to me, and to the world. I do not know if anyone else is alive, but in case something happens to me and someone finds this, it may in some small way explain. Right now I am sitting in a coffee shop in Tucson called Bean There.

They hope that their method will be used for post-approval surveillance of drugs that have already received FDA approval but still carry a risk of toxicity. For instance, one diabetes drug that was on the market was flagged by PrOCTOR and when it was investigated further, it did indeed reveal that it had been taken from the market in Europe.

Cases like Edward Snowden's are precisely the reason the president's constitutional pardon power exists. Historically, outgoing presidents have often invoked this power in the last days of their terms -- at times on behalf of people who've committed reprehensible acts -- under the premise that mitigating circumstances outweigh the rationale for punishment. President Obama now has the opportunity to use this power proudly, in recognition of one of the most important acts of whistleblowing in modern history. Since Snowden first disclosed documents in 2013 detailing the National Security Agency's mass surveillance programs, we've seen an unprecedented global debate about the proper limits of government spying. This debate has had a transformative effect: on privacy laws and standards, on the security of the devices we depend on to communicate with one another and store sensitive information, and on how we understand our relationship to the institutions that govern us.

Donald Trump claims to be running for president as an outsider. But his campaign has resorted to one of the oldest tricks in the book in touting "Kate's Law." Named after Kate Steinle, who was allegedly fatally shot by a Mexican national in the country illegally, the law would set a mandatory minimum prison sentence of five years for anyone who returns to the United States after having been deported. Trump says this is the bill he'll send to Congress on his first day in the White House. Bills named after sympathetic victims are the worst form of knee-jerk lawmaking, but it's a surefire political vote-getting device.

Webroot has announced the acquisition of CyberFlow Analytics, a firm which specializes in harnessing machine learning technology to automatically detect cybersecurity threats. On Monday, the Broomfield, CO-based company said the move enhances Webroot's "ability to address the explosion of internet-connected devices and an increasingly complex threat landscape." Financial details of the acquisition were not disclosed. CyberFlow Analytics' FlowScape technology is a highlight of the acquisition. The SaaS-based cybersecurity solution, available in versions suitable for the enterprise and SMBs, implements machine learning to sift through network noise to detect patterns and network anomalies associated with potential threats.