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AllianceBernstein (AB) announced Andrew Chin as the firms first Director of Artificial Intelligence.

As a member of ABs Operating Committee with a 27-year career at the firm, Chin previously served as Head of Investment Sciences and Solutions at AB. Throughout his career, he has held various positions, including Head of Quantitative Research and Data Scientist, and served as the firms Chief Risk Officer for over a decade.

The appointment of Andrew to this new position recognizes our companys progress with AI and its future potential, said ABs Chief Operating Officer, Karl Sprules.

In his previous role, he was Head of Investment Sciences and Solutions and a member of the firms Operating Committee. Additionally, he has held several leadership positions in quantitative research, risk management, and portfolio management in the firms New York and London offices since joining AB in 1997.

Chin holds a Bachelors degree in Mathematics and Computer Science, and an MBA in Finance from Cornell University.

As AI continues to play a fundamental and transformative role in enhancing ABs operational, business, and investment research procedures, and improving efficiency across all corporate functions, we look forward to having an industry veteran like Andrew lead our company into the future in this newly created role, added Sprules.

Chin also appreciated the firms commitment to the new role.

This new role signifies the evolution not only of my professional trajectory at AB but also of the increasingly significant role that data science and artificial intelligence are playing across the financial services industry, said Chin.

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AllianceBernstein Names Its First Director of Artificial Intelligence - Funds Society

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Breast cancer is a significant global health concern, with more than 2 million new cases diagnosed and over half a million women dying from the disease annually.(1) Many countries, including Norway, have implemented mammographic screening to detect breast cancer in an early stage of disease development, as an early intervention has clear benefits on the disease outcome. In Norway, all women aged 50 to 69 years are invited to biennial mammographic screening through the national screening program, BreastScreen Norway.(2)

The screening process involves two-view digital mammography independently interpreted by two breast radiologists. Cases of potential malignancy are flagged and discussed in a consensus meeting to decide whether further assessment (recall) is needed. However, the time-consuming task of screening interpretation only returns a rate of examinations positive for breast cancer of about 0.6%, at the same time as there is an increasing shortage of breast radiologists in Norway and globally.

Artificial intelligence (AI) and deep learning have been introduced in various healthcare domains, including radiology, to enhance efficiency and improve patient care. Promising results for the use of AI-assisted interpretation in mammographic screening have quickly emerged and several vendors offer solutions for AI-assisted breast cancer detection. Most of these AI algorithms will, based on mammography image analysis, provide a risk of malignancy score a probability of cancer being present in the image or examination. By utilising AI in BreastScreen Norway, we may be able to reduce the interpretation volume for the radiologists without compromising the quality of the screening program. AI can analyse mammograms with high accuracy, reducing the burden of incorrect diagnoses and optimising treatment decisions. AI can also assist in assessing breast density, predicting individual risk levels, and evaluating image quality, providing valuable insights for personalised screening approaches.

In 2018, BreastScreen Norway and the Norwegian Computing Centre set forth to develop an AI algorithm for mammography image interpretation. Through two projects, funded by the Research Council of Norway, mammograms from more than 750,000 screening examinations performed in BreastScreen Norway have been included in the development of an advanced AI algorithm, designed to select mammograms with low suspicion of malignancy.

Mammograms from another 650,000 examinations will be used to further develop the algorithm to become even more robust and reliable. Preliminary results assessing the performance of the current version of the AI algorithm have shown the in-house algorithm to be comparable to commercially available algorithms.(3) To be able to use the in-house algorithm in BreastScreen Norway, its clinical value must be evaluated. This will include long and potentially costly processes to secure that the algorithm complies with EU regulations to achieve CE-marking (Conformit Europenne).

BreastScreen Norways now comprehensive database of mammograms from more than one million screening examinations, enables retrospective studies using commercially available and CE-marked breast AI products for different purposes.

In mammographic screening, AI-assisted screening can be included in different modes:

Retrospective analyses from BreastScreen Norway show that screening mammograms were assigned to the highest risk score by AI in 86-89% of screen-detected cancer cases.(4,5)

Furthermore, the highest risk score was assigned in 45% of the screenings where an interval cancer was later diagnosed (cancer detected in the period between to screenings, based on patient-experienced symptoms). In a triage scenario defining 50% of the examinations with the highest AI scores as positive and the remaining 50% as negative, 99.3% of the screen-detected and 85.2% of the interval cancer cases were classified as positive, leaving us to assume that only 0.7% of the screen-detected cancers were classified as false negative for cancer by the AI system and 15% of the interval cancers are potentially true interval cancers, i.e. not missed by the previous screen but indeed have become detectable in the period between two mammographic screenings.(6)

Before breast AI can be implemented into clinical practice, available algorithms must be thoroughly tested in clinical studies exploring algorithm performance, safety, and reliability, as well as patient outcomes, and ethical and legal aspects, including discrepancies between AI and radiologists. Several challenges and questions remain, including how to integrate AI in breast cancer screening, what is considered an acceptable threshold at which AI can be trusted as an independent reader alongside a radiologist, and what is the impact of AI on readers consensus, recall, and breast cancer detection rates, as well as whether implementation of AI indeed alleviates the workload of radiologists? A natural next step on the road to implementing AI in BreastScreen Norway is to test AI-assisted image interpretation in a real-life screening environment.

Therefore, BreastScreen Norway is starting a randomised controlled trial, comparing AI-assisted mammographic screening with the current standard of care (independent double reading). The trial aims to test different modes of AI-assisted image interpretation and breast AI products, using AI risk score to stratify examinations for single or double reading.

The use of AI will also open avenues for including additional risk factors in the stratification process during screening, including environmental and behavioral factors and research-based factors such as genetics in an overall genetic risk for breast cancer based on inherited genetic variants (polygenic risk scores, PRS).

An academic collaboration between members of the Oslo Cancer Cluster (OCC), Norwegian hospitals, the University of Oslo and the company Antegenes in Estonia has shown that stratification of women based on genetic risk can identify women at several-fold higher risk for breast cancer before the current screening age.(7) Such information can be leveraged to invite women at high risk into the screening earlier, whilst offering women at very low baseline genetic risk a later age of screening startup. Such scenarios may benefit the overall detection rate and alleviate the burden on the breast radiographers, but such additional approaches will need thorough exploration in comprehensive studies before entering the road towards clinical implementation.

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After 28 years spent literally or figuratively in Silicon Valley, Ive grown increasingly concerned about AIs potential to cause widespread, permanent unemployment. So I clambered out of the valley and into the Ivory Tower to share my fears with Israels leading economists. Turns out, the view from the Tower is wildly different from the Valley. Not a good thing.

The view from the Valley is that AI will achieve human-level intelligence within a few years, leading to rising unemployment.

Leopold Aschenbrenner, a former superintelligence researcher at OpenAI, says: We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word... That doesnt require believing in sci-fi; it just requires believing in straight lines on a graph.

Avital Balwit, chief of staff at Anthropic, says: I am 25. These next three years might be the last few years that I work. I stand at the edge of a technological development that seems likely, should it arrive, to end employment as I know it.

Consequently, tech rivals like Sam Altman, Mark Zuckerberg, and Elon Musk agree on the need to prepare for permanent mass unemployment with universal basic income.

Whos right? I hope its the economists, but Id wager on the technologists for three reasons:

The Great Crash of 1929 saw 90% of the stock markets value vanish. A few days prior, the prominent economist, Irving Fisher, pronounced that stock prices have reached what looks like a permanently high plateau. Economists have missed every crash since, prompting the IMF to conclude that economists are notoriously poor at spotting a crisis coming, and that there is little evidence that forecasts at horizons of two to five years contain much predictive content.

Add technology to the mix, and the economists record goes from notoriously poor to comical. McKinsey, for example, pronounced that mobile phones will never be a mass market, while Nobel-winning economist Paul Krugman predicted the Internets impact would be no greater than the fax machines. In retrospect, Krugman conceded that most macroeconomics of the last 30 years was spectacularly useless at best and positively harmful at worst.

In contrast, technologists have an impressive record in predicting key milestones decades in advance. Writing in the 80s and 90s, computer scientist Ray Kurzweil accurately forecasted to within a couple of years the arrival of the Internet, smartphones, voice recognition, self-driving cars, and virtual reality. In 1990, he predicted that human-level AI would arrive in 2029, a prognostication that has since catapulted from preposterous to prescient.

The difference? The economy is governed by the butterfly effect, while technology is governed by Moores Law, which posits that computational power doubles every two years.

Kurzweil calculated roughly how much compute is needed for each milestone he envisaged, and predicted their realization at the point where these needs intersect with the exponential progression of Moores Law. His track record isnt perfect, but no economist can hold a candle to it.

The second reason is that I find the economists arguments unconvincing. One explanation they offer for their equanimity is that, despite the rise of AI, unemployment has not risen at all. Yet nobody expected generative AI to move the macroeconomic needle so quickly; and in smaller, bellwether sectors, the needle is buried in the red. Freelance job boards, for example, have seen massive drops in demand for writers, web developers, graphic designers, and engineers.

More importantly, when a macroeconomic signal does emerge, I expect it to show that AI augments people rather than making them dispensable right up to the point where it dispenses with them. By way of analogy, consider the story of Bob, a mediocre manager. In Act 1, Bob hires Sam, a wunderkind, who boosts the quality and quantity of Bobs deliverables. The big boss is happy. In Act 2, Sam has learned the ropes and is able to fly solo. Bob looks expensive and incompetent by comparison. In Act 3, Bob gets canned. The End.

The second explanation offered for their poise is that weve seen this movie before and it has a happy ending. Theres full employment today even though 99% of the pre-industrial jobs have vanished. Stay calm and carry on.

But, unlike the industrial revolution, where machines replaced our brawn and we found jobs using our brains, todays machines are set to outperform our brains. What part of our being will we use to earn a living once that happens?!

Oh, and the industrial revolution triggered a century of catastrophic hardship, including mass displacement of skilled workers, and a rush for raw materials that fueled colonialism and wars that claimed tens of millions of lives. Not a movie we want to take our kids to.

AI is approaching human-level performance across the spectrum of intellectual endeavors. At its current rate of progress, AI will soon project onto your screen a talking-head that will shape-shift to be your lawyer, graphic designer, doctor, software engineer you name it. As a rule of thumb, therefore, we should assume that any job that can be done over Zoom today can be done by an AI tomorrow. Ive encountered no credible counterargument to this.

Which leads to my second rule of thumb: employers will replace humans with AI whenever theres a buck to be made. That is the true lesson from the industrial revolution. Ive heard no credible counterargument to this either.

Silicon Valley has tunnel vision. Taken together, you see why, on balance, Id wager on Silicon Valleys predictions for what AI will soon do. But Id never trust the Valley to tell society how to adapt or prepare. When it comes to the societal implications of its technologies, Silicon Valley is spectacularly useless at best and positively harmful at worst. Indeed, recent years have seen devastating unintended consequences of the Valleys innovations, from skyrocketing teen suicides to the radicalization of our society.

Tech titans casually toss out slogans like universal basic income as though UBI is a panacea for the coming age. I favor UBI, but they seem oblivious to the monumental challenges it entails, including staggering costs, elusive sources, and complex second-order effects. Moreover, the societal problems born of mass unemployment wont end with any universal income, let alone a basic one. We need the full engagement by the Ivory Tower, leveraging the expertise of economists, political scientists, and sociologists to navigate these intricate issues.

In the coming years, AI is likely to achieve superhuman intelligence and drive rising unemployment. To my knowledge, no one has articulated a convincing case for how such AI can coexist with full employment, and so we must prepare accordingly. Yet those who see the gathering storm are ill-equipped to prepare for it, while those who know how to prepare dont see it coming.

Aesops Fable tells of two men, one blind, the other lame. Alone they cant survive, so the lame man climbs onto the blind mans back, and united they can navigate safely.

The moral is clear: we cant rely solely on economists predictions or Silicon Valleys optimism. We need technologists who understand AIs potential, economists who can model its impacts, and policymakers who can implement solutions.

The clock is ticking. Our choices today will shape whether AI becomes humanitys greatest achievement or, like the golem of Jewish lore, a force that turns on its master.

The writer is CEO and co-founder of Lemonade (NYSE: LMND), and chairman of the MOSAIC Policy Institute, whose mission it is to ensure that Artificial Intelligence benefits all of Israels society.

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Are Elon Musk's concerns of AI-surged unemployment a real concern? - The Jerusalem Post

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