Silicon Valley's CEOs are just like CEOs everywhere: banal financial engineers, not superheroes and supervillains The financialization of everything is just as real in the boardrooms of technology as it is everywhere else; though the deferential press likes to paint the tech-sector leaders as geniuses, superheroes (Elon Musk as Iron Man), and super-villains (Peter Thiel as Lex Luthor), the reality is that they're basically run-of-the-mill financial engineers, whose major creation […]

Chatbots have captivated the tech world. Facebook, Google, Apple, Microsoft, Amazon, and IBM all heavily invest in conversational platforms. Thousands of developers, businesses, and brands build bots to engage users, but one fact is clear... According to Botanalytics, 40 per cent of a bot's users disengage after one interaction. With thousands of bots launching every month, what growth strategies distinguish successful from useless ones?

What is normal for a machine (or the System under observation) is environment-dependent and ever-changing in real life!

NEW YORK: Microsoft has released a set of 100,000 questions and answers that artificial intelligence (AI) researchers can use to create systems that can read and answer questions as precisely as a human. "The dataset is called MS MARCO, which stands for Microsoft MAchine Reading COmprehension, and can be used to teach artificial intelligence systems to recognise questions and formulate answers and, eventually, to create systems that can come up with their own answers based on unique questions they have not seen before," said Microsoft in a blog post. By providing realistic questions and answers, the researchers said they can train systems to better deal with the nuances and complexities of questions regular people actually ask, including those queries that have no clear answer or multiple possible answers. "Our dataset is designed not only using real-world data but also removing such constraints so that the new-generation deep learning models can understand the data first before they answer questions," added Li Deng, Partner Research Manager of Microsoft's Deep Learning Technology Centre. The MS MARCO dataset is available for free to any researcher who wants to download it and use it for non-commercial applications, Microsoft said.

In an ideal workplace scenario, every employer wants to hire the'right' candidate. With success in today's market hinged on having the right talent on board, organizations cannot afford to hire average or low performers. Getting the right talent on-board, and also in a cost and time-effective manner is a task that is getting more complex with steady rise in the number of applications for each job and the sophisticated nature of the competencies required for various job roles. A typical recruitment process today post sourcing revolves around shortlisting a candidate through his/her resume, interaction with a couple of line managers and pre-hiring checks. These interactions, however, do not guarantee any quantifiable indicators of the skills and competencies of a candidate or his/her ability to do a specific job.

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I recently asked Desiree Garcia, designer at IBM, to discuss her experiences in designing for bots, balancing listening to your gut versus listening to stakeholders, and why content-first design is a must. At the O'Reilly Design Conference, Desiree will be presenting a session, Bots may solve some of our problems; here's how they'll put us on the hook for others. What are some of the new challenges bots present for designers? We designers love to talk about empathy all the time and what that means. For creating AI bot technology and bots using AI, I think there's more to building empathy for the user than creating a convincing dialogue, or coming up with ways to help with user frustration if the bot isn't perfect.

Until a few years ago, machine learning algorithms simply did not work very well on many meaningful tasks like recognizing objects or translation. Thus, when a machine learning algorithm failed to do the right thing, this was the exception, rather than the rule. Today, machine learning algorithms have advanced to the next stage of development: when presented with naturally occurring inputs, they can outperform humans. Machine learning has not yet reached true human-level performance, because when confronted by even a trivial adversary, most machine learning algorithms fail dramatically. In other words, we have reached the point where machine learning works, but may easily be broken. This blog post serves to introduce our new Clever Hans blog, in which we will discuss all of the many ways an attacker can break a machine learning algorithm.

LeadGenius, which uses machine learning to provide a marketing and sales tool for businesses, has raised $4 million in a new round of debt and equity funding. Above: LeadGenius cofounders David Rolnitzky (left), chief product officer, and Prayag Narula, CEO. The Berkeley, Calif.-based company said the investors in the round included SJF Ventures, as well as existing investors Lumia Capital and Javelin Venture Partners. LeadGenius uses a combination of machine learning and real human researchers to power its business-to-business (B2B) service platform. The company will use the money to add multi-channel capabilities to LeadGenius for outbound marketing and sales.

What will be the impact of machine learning on economics? NEW YORK, NY - MAY 05: Susan Athey speaks at TechCrunch Disrupt NY 2014 - Day 1 on May 5, 2014 in New York City. The short answer is that I think it will have an enormous impact; in the early days, as used "off the shelf," but in the longer run econometricians will modify the methods and tailor them so that they meet the needs of social scientists primarily interested in conducting inference about causal effects and estimating the impact of counterfactual policies (that is, things that haven't been tried yet, or what would have happened if a different policy had been used). Examples of questions economists often study are things like the effects of changing prices, or introducing price discrimination, or changing the minimum wage, or evaluating advertising effectiveness. We want to estimate what would happen in the event of a change, or what would have happened if the change hadn't taken place.