Rick Ramos (CMO of HealthJoy) has written a great piece on the impact of AI on employee benefits. He discusses how although employee benefits may seem like an odd application of such advanced technology at first glance, once you understand the implications, you can see the true benefit AI can have. Companies spend on average 25 percent to 40 percent of an employee’s salary on benefits, according to Joseph Hadzima Jr., a senior lecturer at MIT Martin Trust Center for Entrepreneurship. Employee benefits are often the second line item in a company’s budget, and these benefits have been serviced with minimal technology until this point.
Our chatbot can automatically pull insurance
information and find a local provider who will take each individual’s insurance. This costs us pennies in server time to perform for thousands of members across the U.S. Without artificial intelligence, an outreach of this scope would be next to impossible and cost hundreds of thousands of dollars.
In this post, Ben Lorica shares slides and notes from a talk he gave this past September at Strata Data NYC offering suggestions to companies interested in adding machine learning capabilities. The information stems from conversations with practitioners, researchers, and entrepreneurs at the forefront of applying machine learning across many different problem domains. As
with any technology or methodology, a successful machine learning project begins with identifying the right use case. There are many possible applications of machine learning—recommenders and reducing customer churn, for example.
To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models. Companies are just beginning to use and deploy machine learning across their products. Tools continue to be refined, and best practices are just beginning to emerge.
Google Cloud has signed a partnership with Cisco to bridge their two technological worlds, in a bid to catch up with Amazon Web Services and its domination of the fast-growing cloud computing market. Under the terms of the agreement, the two companies are investing in ways for Cisco customers to easily bring their applications and data from their existing data centers up into the Google Cloud. For Cisco, it’s the first cloud computing partnership of this type that the company has ever signed.
These tech titans have something to offer each other: Google Cloud gets better access to Cisco customers, helping further its ambitions of conquering the enterprise as it jockeys to move up from its third-place position in the cloud wars. And Cisco gets to offer its customers an easy way to take advantage of Google’s scale and cutting-edge tech.
The number of connected IoT devices worldwide will jump 12% on average annually, from nearly 27B in 2017 to 125B in 2030, according to new analysis from IHS Markit.
“The emerging IoT movement is impacting virtually all stages of industry and nearly all market areas — from raw materials to production to distribution and even the consumption of final goods,” observes Jenalea Howell, research director for IoT connectivity and smart cities at IHS Markit.
John Giannandrea, Google’s head of artificial intelligence, told a conference audience earlier this year that his main concern with AI isn’t deadly super-intelligent robots, but ones that discriminate. “The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased,” he said. His fears appear to have already crept into Google’s own products.
Two weeks ago, Andrew Thompson (Motherboard) experimented with the API for a project he was working on. He began feeding it sample texts, and the analyser started spitting out scores that seemed at odds with what he was giving it. He found that ‘Jew’, ‘gay black woman’, and ‘homosexual’ and more were analysed negatively.
It looks like Google’s sentiment analyser is biased, as many artificially intelligent algorithms have been found to be. AI systems, including sentiment analysers, are trained using human texts like news stories and books. Therefore, they often reflect the same biases found in society. We don’t know yet the best way to completely remove bias from artificial intelligence, but it’s important to continue to expose it.
A new study from MIT’s Media Lab posits that the smaller the city, the greater the impact it faces from automation. The finding, they say, could encourage legislators to pay special attention to workers in smaller cities and offer them support services.
They say that bigger cities have a disproportionately large number of jobs for people who do cognitive and analytical tasks, such as software developers and financial analysts—occupations that are less likely to be disrupted by automation. Smaller cities have a disproportionate amount of routine clerical work, such as cashier and food service jobs, which are more susceptible.
A new Republican technology firm that hopes to make a splash in 2018 is debuting artificial intelligence-enhanced micro-targeting and data analytics. Genus AI, backed by $1M in seed money, according to a company spokesman, is headquartered in London. That’s where the personnel behind the artificial intelligeince technology are based. But the firm, scheduled to launch on Tuesday, is opening a Washington, DC, office.
Jesse Kamzol, former chief data officer at the Republican National Committee, has been hired to run it. Kamzol worked at the RNC from 2015-17, the height of operations for the committee’s modernised, campaign-style data and field programs that were the foundation of President Trump’s ground game.
Now AlphaGo Zero is being set to tasks outside of the 19×19 Go board, according to DeepMind co-founder Demis Hassabis. “Drug discovery, proteins, quantum chemistry, material design—material design, think about it, maybe there is a room-temperature superconductor out and about there,” Demis Hassabis, CEO and Founder of DeepMind. “I used to dream about that when I was a kid reading through my physics books. That would be the Holy Grail, a superconductor discovery.”
But the technology’s use is still nascent; three experts who spoke to Quartz attributed that to the relatively small amount of data. Simulators, like Zero requires, are built on having enough data to predict how an action would take place in the real world—we just haven’t done enough experiments to accurately build a versatile simulator. Even if we did, the molecular world is a lot more complex than a Go board, says Evan Reed, who leads the computational materials science group at Stanford University.