By 2017, it became clear that big tech companies were deeply interested in AI. In the same year, Pitchbook reported that companies around the world had spent USD 21.3 billion on AI-related mergers and acquisitions, an amount believed to be 26 times more than its value in 2015. Jeff Wilke, former chief executive of Amazon worldwide consumer and close ally to company CEO Jeff Bezos then stated, "If you're a tech company and you're not building AI as a core competence, then you're setting yourself up for an invention from the outside." Between 2000 and 2016, companies like IBM and Microsoft were already investing heavily in AI research. Google and Facebook were only moderately involved in AI research and hiring researchers depending on how profitable the project they were working on was.

Speech recognition systems are deep-rooted with bias toward people of color, a new study reveals. Stanford researchers found these technologies from Amazon, Apple, Google, IBM and Microsoft make twice as many errors when interpreting language from black people than words spoken by whites. The team fed systems with nearly 2,000 speech samples from 115 individuals, 42 whites and 73 blacks, and found the average error rate for whites was 19 percent and 35 percent for blacks. Apple was found to perform the worst out of the group with a 45 percent error rate for black speakers and 23 percent for white speakers. Those involved with the study believed the inaccuracies are due to data sets used to train the systems are designed predominately by white people.

Machine learning algorithms process vast quantities of data and spot correlations, trends and anomalies, at levels far beyond even the brightest human mind. But as human intelligence relies on accurate information, so too do machines. Algorithms need training data to learn from. This training data is created, selected, collated and annotated by humans. And therein lies the problem.

Reported by the New York Times, new tests of facial recognition technology suggest that Amazon's system has more difficulty identifying the gender of female and darker-skinned faces compared with similar facial recognition technology services provided by IBM and Microsoft. Amazon's Rekognition is a software application that sets out to identify specific facial features by comparing similarities in a large volume of photographs. The study is of importance, given that Amazon has been marketing its facial recognition technology to police departments and federal agencies, presenting the technology as an additional tool to aid those tasked with law enforcement to identify suspects more rapidly. This tendency has been challenged by the American Civil Liberties Union (See: "Orlando begins testing Amazon's facial recognition in public"). The new study comes from Inioluwa Deborah Raji (University of Toronto) and Joy Buolamwini (Massachusetts Institute of Technology) and it is titled "Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products."

Facial-recognition software developed by Amazon and marketed to local and federal law enforcement as a powerful crime-fighting tool struggles to pass basic tests of accuracy, such as correctly identifying a person's gender, new research released Thursday says. Researchers with M.I.T. Media Lab also said Amazon's Rekognition system performed more accurately when assessing lighter-skinned faces, raising concerns about how biased results could tarnish the artificial-intelligence technology's use by police and in public venues, including airports and schools. Amazon's system performed flawlessly in predicting the gender of lighter-skinned men, the researchers said, but misidentified the gender of darker-skinned women in roughly 30 percent of their tests. Rival facial-recognition systems from Microsoft and other companies performed better but were also error-prone, they said. The problem, AI researchers and engineers say, is that the vast sets of images the systems have been trained on skew heavily toward white men.

Machine learning algorithms process vast quantities of data and spot correlations, trends and anomalies, at levels far beyond even the brightest human mind. But as human intelligence relies on accurate information, so too do machines. Algorithms need training data to learn from. This training data is created, selected, collated and annotated by humans. And therein lies the problem.

For all their differences, big tech companies agree on where we're heading: into a future dominated by smart machines. Google, Amazon, Facebook, and Apple all say that every aspect of our lives will soon be transformed by artificial intelligence and machine learning, through innovations such as self-driving cars and facial recognition. Yet the people whose work underpins that vision don't much resemble the society their inventions are supposed to transform. WIRED worked with Montreal startup Element AI to estimate the diversity of leading machine learning researchers, and found that only 12 percent were women. That estimate came from tallying the numbers of men and women who had contributed work at three top machine learning conferences in 2017.

For all their differences, big tech companies agree on where we're heading: into a future dominated by smart machines. Google, Amazon, Facebook, and Apple all say that every aspect of our lives will soon be transformed by artificial intelligence and machine learning, through innovations such as self-driving cars and facial recognition. Yet the people whose work underpins that vision don't much resemble the society their inventions are supposed to transform. WIRED worked with Montreal startup Element AI to estimate the diversity of leading machine learning researchers, and found that only 12 percent were women. That estimate came from tallying the numbers of men and women who had contributed work at three top machine learning conferences in 2017.

For all their differences, big tech companies agree on where we're heading: into a future dominated by smart machines. Google, Amazon, Facebook, and Apple all say that every aspect of our lives will soon be transformed by artificial intelligence and machine learning, through innovations such as self-driving cars and facial recognition. Yet the people whose work underpins that vision don't much resemble the society their inventions are supposed to transform. WIRED worked with Montreal startup Element AI to estimate the diversity of leading machine learning researchers, and found that only 12 percent were women. That estimate came from tallying the numbers of men and women who had contributed work at three top machine learning conferences in 2017.

NEW YORK--(BUSINESS WIRE)--Accenture (NYSE:ACN) has joined the Partnership on AI, a prestigious alliance of businesses, researchers, academics and policy makers that work to advance the understanding of artificial intelligence (AI) technology and develop best practices on the challenges and opportunities within the field. Accenture joins other prominent industry leaders who share its commitment to advancing the field of AI so that it benefits people and society. Accenture will actively contribute its own insights, research and capabilities in areas including Responsible AI, and will dedicate people and resources to collaborate with Partnership on AI members to help address important global challenges such as food, diversity, inclusion, health, education and economic opportunity. "We are thrilled to join the Partnership on AI. We believe businesses, governments and academia have a critical role in co-innovating and applying AI to invent new educational, workforce and business models that are both responsible and responsive to the needs of people and society. It's up to us to ensure AI can truly be a force for public good," said Paul Daugherty, Accenture's chief technology & innovation officer.