Lifeboat Foundation

The model expects to generate $203 million in revenue by 2030 from its new health promotion business.

How would you feel if someone offered you a stay at a hotel, but with a catch: they would watch you sleep and collect data from your body? Would you be curious, excited, or creeped out? Well, that’s exactly what a Japanese IT company plans to do as part of its new health promotion business.

NTT’s bold plan

Continue reading “Why a Japan firm plans to track sleep pattern of 10 million hotel guests” »

This blog post is tailored to capture the interest of a professional management and executive audience, combining theoretical insights with practical advice. It aims to provoke thought and encourage a proactive approach to the integration of AI in the business world.

As we venture deeper into the digital era, the scope and possibilities of data and artificial intelligence (AI) in human resources (HR) are expanding at an unprecedented rate.

Johnson & Johnson deployed an AI-based writing tool, Textio, to identify unconscious bias in their job listings. Upon identifying a masculine tilt in the language of many of their job postings, they made some AI-driven adjustments that led to a 9% uptick in female applicants.

Unilever employs AI to streamline the initial stages of its recruitment process. Candidates are asked to play a number of games that test their logic, aptitude, reasoning, and appetite for risk. Then the HR team uses machine learning algorithms to assess candidates’ suitability for the role they have applied for, by matching their profiles against previously successful employees. This approach has not only improved the efficiency of Unilever’s recruitment process but also provided a more engaging candidate experience.

Continue reading “The True Value Of Data And AI In Human Resources” »

Nolej’s cofounder, Bodo Hoenen, used AI to help design an exoskeleton for his paralysed daughter, Lorelei.

On their way to building fully autonomous vehicles, self-driving car makers are facing a tall task: training their AIs to be able to respond reliably to any and all scenarios that a car, truck or bus might encounter as well as, or hopefully better, than a human would. Today, a startup with a platform to help with that challenge is announcing a sizeable round of funding to take those strategies up a gear.

Foretellix, which builds verification and validation solutions to test the full range of driver assistance and autonomous systems that are coming out on the market, has closed its Series C at $85 million. The round includes financial investors alongside strategic backers from the automotive and chip industries, a signal of who is already doing business with Foretellix, as well as the longer business trajectory for the startup.

The round is being led by Israeli VC 83North, with Singapore’s Temasek and carmaker Isuzu investing alongside Woven Capital (Toyota’s venture fund), Nvidia, Artofin and previous backers MoreTech, Nationwide, Volvo Group VC, Jump Capital, Next Gear Ventures and OurCrowd. Foretellix may ring a bell for readers: The first close of this round was in May of this year (at $43 million).

The potential impact of generative AI on the economy, society, and work is polarizing, swinging from the positive benefits of a technological revolution to doomsday scenarios. The authors have come to think about this issue as points on a spectrum and have created a sports analogy to help think about it: AI tools can range from steroids, to sneakers, to a coach, each representing a different relationship between human users and the technology. Steroids elevate short-term performance, but leave you worse off in the long term. AI-powered tools can instead be used to augment people’s skills and make them more productive — much like a good running sneaker. On the most desirable end of the spectrum, AI-powered tools can be used like a coach that improves people’s own capabilities. This framework can be used to help conceptualize how we might craft AI-based tools that enhance rather than diminish human capabilities.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.368607 data-title= A Sports Analogy for Understanding Different Ways to Use AI data-url=/2023/12/a-sports-analogy-for-understanding-different-ways-to-use-ai data-topic= AI and machine learning data-authors= Jake M. Hofman; Daniel G. Goldstein; David M. Rothschild data-content-type= Digital Article data-content-image=/resources/images/article_assets/2023/11/Nov23_22_200404124-001-383x215.jpg data-summary=

Will next-gen tools be used as a steroid, sneaker, or coach?

AI can’t transform your business overnight, he said.

OpenAI may be chasing after enterprise users, but some of its executives warn people not to expect the technology to change their businesses quickly.

Brad Lightcap warns that AI is still in its infancy and can’t bring back lost revenue yet.

Combining AI with traditional wet lab work creates a virtuous circle from lab to data and back to the lab.

AI, with the right data, can span all of these scales and make sense of the data we collect on all of them. It’s poised to accelerate basic science, the business of biotechs, the behemoth pharmaceutical companies, and the broader bioeconomy.

One of the big questions GiveDirectly is trying to answer is how to direct cash to low-income households. “Just give cash” is a fun thing to say, but it elides some important operational details. It matters whether someone gets $20 a month for two years or $480 all at once. Those add up to the same amount of money; this isn’t a Side Hustle King situation. But how you get the money still matters. A certain $20 every month can help you budget and take care of regular expenses, while $480 all at once can give you enough capital to start a business or another big project.

The latest research on the GiveDirectly pilot, done by MIT economists Tavneet Suri and Nobel Prize winner Abhijit Banerjee, compares three groups: short-term basic income recipients (who got the $20 payments for two years), long-term basic income recipients (who get the money for the full 12 years), and lump sum recipients, who got $500 all at once, or roughly the same amount as the short-term basic income group. The paper is still being finalized, but Suri and Banerjee shared some results on a call with reporters this week.

By almost every financial metric, the lump sum group did better than the monthly payment group. Suri and Banerjee found that the lump sum group earned more, started more businesses, and spent more on education than the monthly group. “You end up seeing a doubling of net revenues” — or profits from small businesses — in the lump sum group, Suri said. The effects were about half that for the short-term $20-a-month group.