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With artificial intelligence progressing at a rate that can generously be described as unprecedented, many students considering college are wondering what a traditional degree may have to offer in these rapidly changing times. In fact, AI skills and background education were top of the list of factors taken into account as LinkedIn assembled its first-ever list of top colleges, which was released on August 12. While the list also prioritizes other categories culled from its dataincluding hiring rates, job mobility, and alumni networksthe special breakout category that many will surely be most interested in is the one that shows the highest percentages of grads entering the workforce in AI-focused jobs. Many grads are probably concerned about the ability of traditional four-year programs to equip them for this new job landscape, which is understandable as most professionals are currently unclear about how it will impact their companies futures. According to LinkedIn, 81% of C-suite executives are favoring job candidates that are comfortable with AI tools. Which schools are most AI focused? The California Institute of Technology (Caltech) comes in at the top of two lists, including the highest percentage of recent graduates going into AI fields, and the highest percentage of new grads listing AI literary and engineering skills on their LinkedIn profiles. LinkedIn also found that Caltech offers a range of AI-focused classes and overall programs, including AI4Science initiative, which the website describes as an initiative aimed at bringing AI researchers with experts from other disciplines to push modern AI tools into every area of science and engineering. The Massachusetts Institute of Technology (MIT) came in second in both categories, with University of California-Berkeley, Stanford, Carnegie Mellon University, Harvey Mudd, and University of California-San Diego also appearing on both lists. Schools with the highest percentage of recent grads going into AI occupations California Institute of Technology Massachusetts Institute of Technology Carnegie Mellon University Harvey Mudd College University of California-Berkeley Stanford University University of Rochester University of California-San Diego University of Chicago Harvard University Schools with the highest percentage of recent grads adding AI-related skills on LinkedIn profiles California Institute of Technology Massachusetts Institute of Technology Harvey Mudd College Stanford University Carnegie Mellon University University of California-Berkeley University of California-San Diego Brown University Georgia Institute of Technology Princeton University While these two breakout categories are particularly relevant for those considering tech fields, the larger list looks at more traditional considerations for students, including job placement, recruiter demand, career success, network strength, and knowledge breadth. They also specify notable skills listed by grads from the top schools. For the overall list, Princeton took home the top spot, with Duke and the University of Pennsylvania taking the second and third spots. The entire list can be found on LinkedIns website.
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E-Commerce
Ten years from now, it will be clear that the primary ways we use generative AI circa 2025rapidly crafting content based on simple instructions and open-ended interactionswere merely building blocks of a technology that will increasingly be built into far more impactful forms. The real economic effect will come as different modes of generative AI are combined with traditional software logic to drive expensive activities like project management, medical diagnosis, and insurance claims processing in increasingly automated ways. In my consulting work helping the worlds largest companies design and implement AI solutions, Im finding that most organizations are still struggling to get substantial value from generative AI applications. As impressive and satisfying as they are, their inherent unpredictability makes it difficult to integrate into the kind of highly standardized business processes that drive the economy. Agentic vs. Interpretive Agentic AI, which has been getting tremendous attention in recent months for its potential to accomplish business tasks with little human guidance, has similar limitations. Agents are evolving to assist with singular tasks such as building websites quickly, but their workflows and outputs will remain too variable for large organizations with high-volume processes that need to be predictable and reliable. However, the same enormous AI models that power todays best-known AI tools are increasingly being deployed in another, more economically transformative way, which I call interpretive AI. And that is whats likely to be the real driver of the AI revolution over the long term. Unlike generative and agentic AI, interpretive AI lets computers understand messy, complex, and unstructured information and interpret it in predictable, defined ways. Using much of the same IT infrastructure, the emerging technology can power large organizations complex processes without requiring human intervention at each step. Use cases Some interpretive AI applications are already in use. For example, doctors are saving significant time by using interpretive AI tools to listen to conversations with patients and fill in information on their electronic health record interfaces to track care and facilitate billing. In the near future, the technology could determine fault in auto accidents based on police reports written in any of thousands of different formats, or process video recorded from a laptop screen as someone edits a presentation to provide teammates with an automated update on work completed. The applications are wide-ranging and span all manner of industries. Based on estimates for areas such as coding and marketing where generative AI is most applicable, interpretive AI could unlock 20% to 40% productivity gains for the half of GDP that comes from large corporations. First, though, they must commit to developing a comprehensive, long-term strategy involving multiple business functions and careful experimentation, and change entrenched processes and work culture norms that slow its adoption. Done right, the obstacles are surmountableand the payoff could be massive. A different application of generative AI models One of the most basic drivers of economic growth is the ongoing effort to standardize and scale up a particular process, making it faster, cheaper, and more reliable. Think of factory assembly lines enabling mass production, or the internets codification of computer communication protocols for use across disparate networks. Generative AI has been, on the whole, disappointing when it comes to automation. For example, many firms have tried to use generative AI chatbots to reduce the time their human resources staff spends answering employees questions about internal policies. However, the open-ended output from such systems requires human review, rendering the labor savings modest at best. The technology seems to inherit much of the unpredictability of humans along with its ability to mimic their creative and reasoning skills. Agentic AI promises to do complicated work autonomously, with smart AI agents developing and executing plans for achieving goals step-by-step, on the fly. But again, even when agents become smart enough to help a typical knowledge worker be more productive, their outputs will be quite variable. Enter interpretive AI. For the first time, computers can usefully process the meaning of human language, with all its nuance and unspoken context, thanks to the unprecedentedly large models developed by firms like Open AI and Google. Interpretive AI is the mechanism for using the models to exploit this revolutionary advance. Until now, computers ability to capture, store, aggregate, summarize, and evaluate a large organizations activities were limited to those that were easy to quantify with data. Interpretive AI can quickly and precisely execute these functions for many other important activities, at a vast scale and at minimal marginal cost. For instance, no longer will businesses need manual processes to monitor and manage levels of activity and progress in knowledge-worker tasks such as coding a feature into a software solution or developing a set of customer-specific outreach strategies, which usually require dedicated middle management staff to collect information. Companies can make productivity gains by using interpretive AI for a range of other previously hard-to-measure employee issues as well, including the tone and quality of their interactions with customers, their cultural norms in the workplace, and their compliance with office policies and behavioral expectations. Transforming the management of knowledge work The use of interpretive AI will enable the widespread transformations that unlock newly efficient ways of working at large organizations (which are responsible for organizing and producing most of the worlds goods and services). It will dramatically reduce the need for extensive, costly, slow-moving, and unenjoyable middle management work to coordinate complex interrelated programs of activities across teams and disciplines. Even better, it can efficiently understand operationally vital but opaque aspects of how work happens, such as the decades worth of legacy code and data that make even minor technology process changes time-consuming and challenging for any long-lived enterprise. Of course, interpretive AI is not mutually exclusive with generative and agentic AIagain, its simply a different way to use the powerful models that power those technologies. A decidedly unsexy way, certainly, but for businesses looking for ways to maximize the economic impact of AI over the next ew years, its just the unsexy they need.
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E-Commerce
A new study from Cornell University goes against the grain of popular thought, arguing that left-handed people aren’t necessarily more creative than their right-handed counterparts after all. It’s research that hits close to home for this writer. From an early age, I’ve worn my left-handedness as a badge of pride. As a kid, I always felt different from the other students in class, because I had to use a left-handed desk. Back then, I also had to use special scissors in home economics, bat on the “wrong” side of the plate at softball . . . the list goes on. But despite the minor inconveniences, it was a label I readily embraced because I was told I was “special” (only 10% of the population is left-handed) and, perhaps most of all, because I knew I was in good company. Who wouldn’t want to be a member of a club that includes Michelangelo, Leonardo da Vinci, Aristotle, one of the Beatles, Bill Gates, Nikola Tesla, Marie Curie, Babe Ruth, Bart Simpson, Oprah, and Jerry Seinfeld? In fact, five out of the last eight presidents have been left-handed: Gerald Ford, Ronald Reagan, George H.W. Bush, Bill Clinton, and Barack Obama. (President Trump is a rightie.) To this day, I still make a mental note of who is and is not a lefty. Picasso and John Lennon aren’t, but Paul McCartney is. So is my best friend, Gaby, my editor, Connie, and my boss, Christopher. It’s a secret club we lefties share, believing there is something just a little special, a little more creative about us. That’s why the new research from Cornell stopped me in my tracks. The science of creativity In Handedness and Creativity: Facts and Fictions, published in the Psychonomic Bulletin & Review, researchers argue that while there’s a plausible link between creativity and handedness based on theories that look at the neural basis of creativity, they found no evidence that left- or mixed-handed individuals are more creative than right-handers. In fact, they even found right-handers scored statistically higher on one standard test of divergent thinking (the alternate-uses test). “The data do not support any advantage in creative thinking for lefties, said the studys senior author, Daniel Casasanto, associate professor of psychology at Cornell. And while the Cornell researchers acknowledge that left- and mixed-handers may be overrepresented in art and music, they argue that southpaws are underrepresented in other creative professions, like architecture. When determining which professions constitute creative fields, researchers drew on data from nearly 12,000 individuals in more than 770 professions, which were ranked by the creativity each requires. By combining originality and inductive reasoning, they concluded that physicists and mathematicians rank alongside fine artists as having the most creative jobs. Using this criteriaand considering the full range of professionsthe researchers found that left-handers are underrepresented in fields that require the most creativity. The focus on these two creative professions where lefties are overrepresented, art and music, is a really common and tempting statistical error that humans make all the time, Casasanto said. People generalized that there are all these left-handed artists and musicians, so lefties must be more creative. But if you do an unbiased survey of lots of professions, then this apparent lefty superiority disappears. Casasanto did agree, however, that there are scientific reasons to believe that left-handed people would have an edge in creativity when it comes to “divergent thinking”the ability to explore many possible solutions to a problem in a short time and make unexpected connectionswhich is supported more by the brains right hemisphere. But again, the study revealed that handedness makes little difference in the three most common laboratory tests of its link to divergent thinking; if anything, righties have a small advantage on some tests. Finally, researchers conducted their meta-analysis by crunching the data from nearly 1,000 relevant scientific papers published since 1900. Most were weeded out because they did not report data in a standardized way, or included only righties (the norm in studies seeking homogeneous samples), leaving just 17 studies reporting nearly 50 effect sizes. This may be why the newest study came to a different conclusion than what is held in popular belief or prior scientific literature.
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E-Commerce
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