We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the secondstage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the secondstage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

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Engineering has a white-male problem. Women make up just 14.5 percent of the engineering workforce in the United Kingdom, with ethnic minorities constituting just 8 percent. For Lila Ibrahim, chief operating officer at DeepMind, and Hayaatun Sillem, CEO of the Royal Academy of Engineering, being both female and people of color meant the odds were stacked against them in their industry. But for Sillem, who is the first woman and ethnic minority to hold her position, coming from such a diverse background helped her "to build empathy into her life"--a trait she describes as a superpower. And as for Ibrahim, the daughter of immigrants to the United States, she always felt like the "oddball" growing up in midwestern America.

Take a look at voice applications. When applying a mindful AI approach, and leveraging the power of a global talent pool, developers can account for linguistic elements such as different dialects and accents in the data sets. Many of the people we rely on to crowdsource at Pactera EDGE are not full-time employees, but they develop expertise working regularly in our projects. We use modules and tests to identify and reward those with the strongest capabilities at translation and those that produce the best outcomes for our clients. Establishing a human-centered design framework from the beginning is critical.

Adam Oliner, co-founder and CEO of Graft used to run machine learning at Slack, where he helped build the company's internal artificial intelligence infrastructure. Slack lacked the resources of a company like Meta or Google, but it still had tons of data to sift through and it was his job to build something on a smaller scale to help put AI to work on the dataset. With a small team, he could only build what he called a "miniature" solution in comparison to the web scale counterparts. After he and his team built it, however, he realized that it was broadly applicable and could help other smaller organizations tap into AI and machine learning without huge resources. "We built a sort of mini Graft at Slack for driving semantic search and recommendations throughout the product. And it was hugely effective … And that was when we said, this is so useful, and so powerful if we can get this into the hands of most organizations, we think we could really change the way people interact with their data and interact with AI," Oliner told me.

In a recent article, WIRED senior writer Tom Simonite talked to Kate Crawford, author of Atlas of AI, to explore the ethical issues facing artificial intelligence and machine learning technologies. "We're relying on systems that don't have the sort of safety rails you would expect for something so influential in everyday life," notes Crawford. "There are tools actually causing harm that are completely unregulated." When people that aren't in the industry hear me say that artificial intelligence and machine learning can become forces for positive change in society, they ask me to explain why these technologies have been mired in controversy for more than a decade. And why ethical issues seem to be getting worse versus getting better. Indeed, in recent years several high-profile cases of ML technologies causing harm to marginalized parts of society have captured headlines.

When the three founders of Faros AI were working at Salesforce, they helped develop the company's artificial intelligence, known as Einstein. While the goal of Einstein was to help companies become more data-driven, the engineering team building it experienced the same pain of tracking engineering operations data as any other company. Faros AI CEO and co-founder Vitaly Gordon said that despite Salesforce's vast resources, they still suffered from a dearth of data and a lack of adequate tools for collecting it. "We were scaling that operation within Salesforce and working with I think close to 10,000 customers, but we realized that we actually were not practicing what we preach as a technical organization [when it came to making use of data]," Gordon said. That their engineering team lacked the kinds of tools it was building for sales and marketing teams to put data to work was a real eye-opener.

Any AI-based product, ranging from Alexa to Netflix, needs reliable data to teach itself how to be more effective. But what happens when AI lacks enough real data to train itself? This is where synthetic data comes into play. Synthetic data consists of data generated with the assistance of AI. Synthetic data is based on a set of real data.

Shekhar Gupta has been involved in high technology for the last 25 years, either working for fortune 100 companies or his start-ups. He had his own technology companies that he has developed successfully. Shekhar has also been engaged in different sectors including the cloud since 2001, AI/ML since 2006, and blockchain since 2011. On the other hand, he has developed multiple products and networks using these technologies in various industries such as Telecom, Govtech, EdTech, and even AgTech now. Recently, Shekhar started an animal health company called MyAnIML that uses AI to analyze an animal's face and predict a disease before any symptoms are visibly seen and the animal becomes sick and contagious.