Nearly all HR leaders say their department will use software and algorithms to reduce labor costs in a 2023 recession, but only half are completely confident their tech will produce unbiased recommendations.
Entering 2023, the dreaded “R” word—recession—is top of mind for companies around the country. In a Capterra survey of 300 HR leaders in the U.S., 72% say their employer has already started preparing for a possible recession, while 24% plan to start preparing soon.*
As in previous economic downturns, organizations will need to figure out ways to reduce labor costs, including deciding which employees to lay off if it comes to that. Where 2023 differs is that HR is both more strategically involved in these high-level labor decisions and more data-driven than ever before, supported by cutting-edge HR software systems that can aggregate massive amounts of employee information and turn it into actionable insights and recommendations. Read More
Tag Archives: Strategy
5 AI Companies that are Shaping the Future in 2023 | Artificial Intelligence
The Future Of A.I. Businesses With Steph Smith
AI and the future of work: 5 experts on what ChatGPT, DALL-E and other AI tools mean for artists and knowledge workers
From steam power and electricity to computers and the internet, technological advancements have always disrupted labor markets, pushing out some jobs while creating others. Artificial intelligence remains something of a misnomer – the smartest computer systems still don’t actually know anything – but the technology has reached an inflection point where it’s poised to affect new classes of jobs: artists and knowledge workers.
Specifically, the emergence of large language models – AI systems that are trained on vast amounts of text – means computers can now produce human-sounding written language and convert descriptive phrases into realistic images. The Conversation asked five artificial intelligence researchers to discuss how large language models are likely to affect artists and knowledge workers. And, as our experts noted, the technology is far from perfect, which raises a host of issues – from misinformation to plagiarism – that affect human workers. Read More
Artificial intelligence in strategy
AI tools can help executives avoid biases in decisions, pull insights out of oceans of data, and make strategic choices more quickly. And that’s just the beginning.
Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. Read More
Transcript Available Here
AI and the Big Five
The story of 2022 was the emergence of AI, first with image generation models, including DALL-E, MidJourney, and the open source Stable Diffusion, and then ChatGPT, the first text-generation model to break through in a major way. It seems clear to me that this is a new epoch in technology.
To determine how that epoch might develop, though, it is useful to look back 26 years to one of the most famous strategy books of all time: Clayton Christensen’s The Innovator’s Dilemma, particularly this passage on the different kinds of innovations:
Most new technologies foster improved product performance. I call these sustaining technologies. Some sustaining technologies can be discontinuous or radical in character, while others are of an incremental nature. What all sustaining technologies have in common is that they improve the performance of established products, along the dimensions of performance that mainstream customers in major markets have historically valued. Most technological advances in a given industry are sustaining in character…
Disruptive technologies bring to a market a very different value proposition than had been available previously. Generally, disruptive technologies underperform established products in mainstream markets. But they have other features that a few fringe (and generally new) customers value. Products based on disruptive technologies are typically cheaper, simpler, smaller, and, frequently, more convenient to use. Read More
Achieving Individual — and Organizational — Value With AI
Findings from the 2022 Artificial Intelligence and Business Strategy Global Executive Study and Research Project
New research shows that employees derive individual value from AI when using the technology improves their sense of competency, autonomy, and relatedness. Likewise, organizations are far more likely to obtain value from AI when their workers do. This report offers key insights for leaders on achieving individual and organizational value with artificial intelligence in their organizations. Read More
Why big data is not a priority anymore, and other key AI trends to watch
Artificial Intelligence models that generate entirely new content are creating a world of opportunities for entrepreneurs. And engineers are learning to do more with less.
Those were some takeaways from a panel discussion at the Intelligent Applications Summit hosted by Madrona Venture Group in Seattle this week.
“Big data is not a priority anymore, in my opinion,” said Stanford computer science professor Carlos Guestrin. “You can solve complex problems with little data.”
Engineers are more focused on fine tuning off-the-shelf models, said Guestrin, co-founder of Seattle machine learning startup Turi, which was acquired by Apple in 2016. New “foundation” AI models like DALL-E and GPT-3 can hallucinate images or text from initial prompts. Read More
Brief Review — Andrew Ng, AI Minimalist: The Machine-Learning Pioneer Says Small is the New Big
- I’ve taken many his AI courses in deeplearning.ai and was a mentor in one of his courses in Coursera. His courses really strengthen me a lot about the deep learning knowledge.
- This time, I would like to share an article that I’ve read recently, from IEEE Spectrum Magazine in April 2022, namely “Andrew Ng, AI Minimalist: The Machine-Learning Pioneer Says Small is the New Big”
- IEEE Spectrum Magazine is a monthly magazine, which talks about technology of all kinds. It has impact factor of 3.578.
- In this article, Andrew Ng has shared a lot of his valuable visions and broad views about AI, e.g.: NLP, CV, semiconductor manufacturers, and his company, even from his first NeurIPS workshop paper, to recent NeurIPS data centric AI workshop! Read More
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Generative AI: A Creative New World
Humans are good at analyzing things. Machines are even better. Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it’s fraud or spam detection, forecasting the ETA of your delivery or predicting which TikTok video to show you next. They are getting smarter at these tasks. This is called “Analytical AI,” or traditional AI.
But humans are not only good at analyzing things—we are also good at creating. We write poetry, design products, make games and crank out code. Up until recently, machines had no chance of competing with humans at creative work—they were relegated to analysis and rote cognitive labor. But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists.
Generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand. Every industry that requires humans to create original work—from social media to gaming, advertising to architecture, coding to graphic design, product design to law, marketing to sales—is up for reinvention. Certain functions may be completely replaced by generative AI, while others are more likely to thrive from a tight iterative creative cycle between human and machine—but generative AI should unlock better, faster and cheaper creation across a wide range of end markets. The dream is that generative AI brings the marginal cost of creation and knowledge work down towards zero, generating vast labor productivity and economic value—and commensurate market cap. Read More
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