From reports Amazon's same-day delivery is less available in black neighbourhoods to Microsoft's'racist' chatbots, signs of online prejudice are becoming increasingly common. Scientists now say they can spot racist and sexist software using a code that finds out if there is implicit bias in algorithms running on websites and apps. By changing specific variables - such as race, gender or other distinctive traits - the online code Themis claims to know if data is discriminating against specific people. Previous research suggests technology is generally becoming racist and sexist as it learns from humans - and as a result, hindering its ability to make balanced decisions. Themis is a freely available code that mimics the process of entering data - such as making a loan application - into a given website or app.

Artificial intelligence is already capable of creating a staggering array of content. It can paint, write music, and put together a musical. It can write movies, angsty poems, and truly awful stand-up comedy. But does it have ownership over what it produces? For example, an AI at Google has managed to create sounds that humans have not heard before, merging characteristics of two different instruments and opening up a whole new toolbox for musicians to play around with.

Eran Kahana, an intellectual-property lawyer at Maslon LLP and a fellow at Stanford Law School, doesn't believe we should award authorship to AIs. He explains that the reason IP laws exist is to "prevent others from using it and enabling the owner to generate a benefit. An AI doesn't have any of those needs. AI is a tool to generate those kinds of content." "Obviously not the computer", Kahana quips.

Ethical concern about technology enjoys booming popularity, evident in worry over artificial intelligence, threats to privacy, the digital divide, reliability of research results, and vulnerability of software. Concern over software shows in cybersecurity efforts and professional codes.1 The black hats are hackers who deploy software as a weapon with malicious intent, and the white hats are the organizations that set safeguards against defective products. But we have a gray-hat problem--neglect. My impression is that the criteria under which I used to assess student programs--rigorous thought, design, and testing, clean nested conditions, meaningful variable names, complete case coverage, careful modularization--have been abandoned or weakened.

Algorithms are replacing or augmenting human decision making in crucial ways. People have become accustomed to algorithms making all manner of recommendations, from products to buy, to songs to listen to, to social network connections. However, algorithms are not just recommending, they are also being used to make big decisions about people's lives, such as who gets loans, whose résumés are reviewed by humans for possible employment, and the length of prison terms. While algorithmic decision making can offer benefits in terms of speed, efficiency, and even fairness, there is a common misconception that algorithms automatically result in unbiased decisions. In reality, inscrutable algorithms can also unfairly limit opportunities, restrict services, and even improperly curtail liberty.

We take a look at categories where AI startups are beginning to emerge. A majority of them are in the early stages of funding. We take a look at categories where AI startups are beginning to emerge. A majority of them are in the early stages of funding. Artificial Intelligence is being hailed as the new linchpin of the tech industry.

Pamela Fox, Woebot's chief technology officer, center, talks to Alison Darcy, Woebot's founder and chief executive, at the offices of the start-up in San Francisco. Pamela Fox, Woebot's chief technology officer, center, talks to Alison Darcy, Woebot's founder and chief executive, at the offices of the start-up in San Francisco. Fifty years ago, an MIT professor created a chatbot that simulated a psychotherapist. Named Eliza, it was able to trick some people into believing it was human. But it didn't understand what it was told, nor did it have the capacity to learn on its own.

Demis Hassabis, founder of DeepMind, recently spoke out about his vision on how to reach the "next level," of artificial intelligence. His strategy is, predictably, to reconnect with the field of neuroscience, to study natural intelligence, in the hope of mimicking these processes inside the machine. While I have shortly cover my opinion on this before, I want to take another pass over this topic to see if the opinions of multiple high ranking experts are able to make me change my mind about human like artificial intelligence.

Last fall, University of Virginia computer science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. "It would see a picture of a kitchen and more often than not associate it with women, not men," he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to "train" image-recognition software. Two prominent research-image collections--including one supported by Microsoft and Facebook--display a predictable gender bias in their depiction of activities such as cooking and sports.

Have you heard the buzz? Artificial intelligence is taking the legal world by storm--and lawyers are embracing the change, despite their traditional resistance to technology. In today's world, data is growing explosively. While that massive store of data contains correspondingly voluminous and useful information--especially for the practice of law--it also takes massive time to analyze. And then there's the monotony, boredom, and frustration felt by humans who are trying to plow through a Sisyphean task, and the ever-increasing need for speed in response to client, court, and regulatory agency demands.