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Man Vs Machine !FULL!

I use machine learning to categorize US active equity mutual funds as quantitative (reliant on computer models and fixed-rules) or discretionary (reliant on human judgment). I then formulate hypotheses of how their holdings and performance might differ, based on the conjecture that quantitative funds might have more learning capacity but less flexibility to adapt to changing market conditions than discretionary funds. Consistent with those hypotheses, I find that quantitative funds hold more stocks, specialize in stock picking, and engage in more overcrowded trades. Discretionary funds hold lesser-known stocks, switch between picking and timing and outperform quantitative funds in recessions.

Man vs Machine

Arguably the two leaders from each side are Sustainalytics, now owned by Morningstar and representing the analyst-based sustainability research and ratings approach; and Truvalue Labs, which harnesses AI, machine learning and natural language processing to provide investors and companies with corporate sustainability performance insights and assessments.

"Many sustainable investors are skeptical of AI and the idea that we can trust machines. We make a point of emphasizing that the role of AI is to do things humans can't do efficiently in order to make humans work smarter. And good AI depends on being deployed intelligently by humans. Not all AI is created equal," said Kuh.

The contingency/fit analysis performed in this research highlights the complex nature of the use of technology in CRM support capabilities. The benefits of a man vs a machine CRM support capability depend on the support function (whether marketing, sales, service, data access or data analysis), as well as upon the characteristics of the operating environment. Machine-based marketing support is positively related with customer commitment in turbulent markets, and machine-based service support is preferred in technologically turbulent markets. Sales support, on the other hand, is positively related to customer commitment in technologically turbulent markets when performed by man rather than machine.

Powell, A., Noble, C.H., Noble, S.M. and Han, S. (2018), "Man vs machine: Relational and performance outcomes of technology utilization in small business CRM support capabilities", European Journal of Marketing, Vol. 52 No. 3/4, pp. 725-757. -10-2015-0750

Let's go head-to-head with the Draft Day Predictor by offering my own predictions on what the Philadelphia Eagles will do at picks 15 and 18 overall. It's all in the name of providing a comprehensive look at the Eagles' options and how they might be thinking entering a pivotal draft for general manager Howie Roseman & Co. May the best man/machine win.

May 11, 1997 was a watershed for the relationship between man and machine, when the artificial intelligence (AI) supercomputer Deep Blue finally achieved what developers had been promising for decades.

"This is not about man versus machine. This is really about how we, humans, use technology to solve difficult problems," said Deep Blue team chief Chung-Jen Tan after the match, listing possible benefits from financial analysis to weather forecasting.

They have fueled increasingly powerful AI machines with unimaginable amounts of data from their users, serving up remorselessly targeted content and advertising and forging trillion-dollar companies in the process.

For those who need a refresher on this American folk icon, the story goes like this: During the 1800s, railroads started to snake across the U.S., and bands of men would smooth out the land by driving stakes into rock with a big ole hammer (and then filling the holes with explosives). John Henry, an African American, was supposed to be the biggest — in spirit, in appetite, in the bulging of biceps — and best driver of all. When companies started to employ steam-powered drills to make better time, Henry decided to challenge one to a race. He won but, tragically, died of exhaustion following his miraculous feat. The story is based in fact, but the details change with the telling — how big Henry was, for example, or whether he was driving spikes or blasting rock. Regardless, his story remains the benchmark for the many human-machine battles since.

Four albums deep, Xzibit's presence on the microphone rivals that of the better MCs of the latter day. However, on Man vs Machine, Xzibit seems to have lost some of the edge on his lyrical blade, which once tested in underground fires more than a half-decade before he hit the mainstream in 1999. The man who brought listeners "Paparazzi," "Foundation" (from his 1996 debut, At the Speed of Life), and "What U See Is What U Get" (from the 1998 release 40 Dayz & 40 Nightz) managed to avoid the sophomore slump, but instead found the junior jinx with the disappointing 2000 release of Restless, even with Dr. Dre in his corner. While X is still in cahoots with the good Dre here, unfortunately Man vs Machine is a continuation of his lackluster spiral rather than the masterpiece that X fans thought to be inevitable when he linked up with the likes of Dre, Snoop Dogg, and Eminem in the Y2K. Things start off with dark zest on the cleverly worded and sinisterly composed "Release Date," produced not by Dre, but by East Coaster Rockwilder. Dre chimes in with lyrics for the awkward and clunky "Symphony in X Major" and beats for the delightfully raunchy "Choke Me, Spank Me" and the slightly above-workaday "Losin' Your Mind" featuring Snoop Dogg. Also, taking a somewhat shameful page out of P. Diddy's book, producer Jelly Roll turns to early-'80s Toto ("Africa") for inspiration on the painfully inane "Heart of Man." Things pick up some on the Dre camp's retort to a Jermaine Dupri dis on the Eminem-produced "My Name" (featuring some patented Slim Shady punch lines and a G-hook from Nate Dogg) and the cross-continental banger "BK to LA" featuring Brownsville sluggers M.O.P. The heartfelt ode to his mother ("Missin' U") notwithstanding, after the floss and gloss of this release is peeled away, there's a lot more of Xzibit the MC caught in the machine of the hip-hop industry than there is of Xzibit the gifted man.

Many people are afraid of snakes and spiders. But, robots and other machines also create anxiety and trepidation in some humans. While Industry 4.0 technology, such as artificial intelligence, augmented reality, collaborative robots, data analytics and digital twins, now make it easier than ever for humans and machines to work in close proximity, fears persist.

Next, we examine the circumstances under which human analysts retain their advantage, in that a forecast made by an analyst beats the concurrent AI forecast in terms of lower absolute forecast error relative to the ex-post realization (i.e., the actual year-end stock price). We find human analysts perform better for more illiquid, smaller firms, and firms with asset-light business models (i.e., higher intangible assets), consistent with the notion that such firms are subject to higher information asymmetry and require better institutional knowledge or industry experience to decipher. Analysts affiliated with large brokerage houses also stand a higher chance of beating the machine, thanks to a combination of their abilities and the research resources available to them. Moreover, analysts are more likely to have the upper hand when the associated industry is experiencing distress, suggesting that the AI has yet to catch up on relatively infrequent changes such as an industry recession. This is consistent with the limitation of current machine learning and AI models, which lack reasoning functions and thus cannot learn effectively from infrequent events. As expected, AI enjoys a clear advantage in its capacity to process information and is more likely to out-smart analysts when the volume of public information is larger.

If there is some external validity from stock analysis to skilled workers in general, the inference from our study is encouraging news for humans in the age of AI. The complementarity between humans and machines documented in this study also provides guidance about how humans can adapt to survive and thrive in the age of machines. For example, reforming education and professional training to strengthen soft skills and creativity can help human professionals to better prepare for the incoming future.

A lot of the concern about artificial intelligence in the workplace appears to be based on what people have seen in cartoons, read in novels, or watched in sci-fi movies, where the world is portrayed as being overtaken by robots. Now that AI-based systems and applications are gaining ground, people are getting nervous about the role of machines. Will they take over our jobs?

By the same token, humansget smarter based on the information they learn from machine analysis. Peoplecan then apply their higher-order skills to make decisions based on datacoupled with their own knowledge base. AI systems subsequently interpret whathumans do with the information generated; they in turn get smarter based onthese interactions, and systems are refined. This process continues whenever aquery is run, new data is added, and action is taken.

Positions will open up thatembrace new skill sets. Employees who bridge the gap between domain expertiseand technology will be essential, and those who can navigate between business,analytics, and customer service will be in the highest demand. As workersbecome smarter and more dependent on machine learning, they become even morevaluable to their organizations, bringing fresh, creative ideas into theworkplace with unprecedented efficiency.

Machine extrication occurs every day, often in dangerous and complicated situations. In Man vs. Machine, Mark Gregory, Randy Miller, and Mike Meyer effectively demonstrate the tools and techniques required to successfully extricate a victim from various types of machinery. 350c69d7ab


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