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Championship tournaments for computer chess engines moved from onsite competition to online well before many human tournaments made the move last year in response to the COVID-19 pandemic. In recent years the Top Engine Chess Competition, which has been played virtually since 2010, has become the unofficial world computer chess championship.

In recent years, many of these competitions have been won by the open-source chess engine Stockfish, thanks to its ability to conduct deep searches of chess positions enabled by powerful computing. However, in 2019 the Stockfish engine was upended by the LCZero engine, which was developed using a very different approach, employing techniques that develop artificial intelligence. LCZero was launched in 2018 with no chess-specific knowledge other than the basic rules; it learned how to play by analyzing the results of millions of games played by volunteer users. This approach was extremely successful and led to LCZero defeating Stockfish to win TCEC tournaments in 2019 and 2020.

The Stockfish team responded by following the maxim if you cant beat em, join em. In late 2020, a new version of Stockfish was introduced that complemented its deep position searches with a learning function similar to that employed by LCZero. The improved Stockfish has regained its top position among chess engines. In the latest TCEC championship, Stockfish trounced LCZero, with 19 wins and only seven losses in their 100-game match. Other chess engine developers have taken note, and all of the top-rated chess engines now combine classical computing with learning functions.

In the recent match, Stockfish often outperformed LCZero in games that reached unusual positions where deep position searches proved to be more valuable than evaluations that relied on prior learning. In Game 68, the following position was reached after lengthy maneuvering by both sides. LCZero evaluated the position as even, but Stockfish found an opportunity to unbalance the game, to its advantage, by offering a surprising bishop sacrifice.

White to Move

(Stockfish-LCZero Game 68 Move 180)

180Bf6! If black plays 180gxh6? white has 181Rxh6+ Nxh6 182Rxh6+ Kg8 183Qh5 and white forces checkmate in a few moves. After further maneuvering, Stockfish intensified its attack on the black king by offering to sacrifice a second piece its queen.

White to Move

(Stockfish-LCZero Game 68 Move 191)

191Qg5! The queen cannot be taken; 191..hxg5 192Rh8 is checkmate. Black has no satisfactory response. The game continued 191Re8 192Rxh6! Nxh6 193Rxh6 gxh6 194Qxh6 when black must sacrifice its queen to delay checkmate.

Black to Move

(Stockfish-LCZero Game 68 Move 194)

194Qg7 195Bxg7 Rxg7 196f5 exf5 197Qg5. Black cant capture whites e-pawn; 197Rxe5? 198Qd8+ and white is about to checkmate.

197Rf8 198e6 Rc7 Stockfish now maneuvers its King to g5, freeing up the queen to harass the black king and rooks.

199Kc3 Rg7 200Kd4 Rc7 201Ke5 Rg7 202Kf4 Rc7 203 Qh4 Rg7 204Kg5 Re7 205Qf4 Kg7 206Qd6 Rfe8 207Qe5+ Kg8 208Qf6 LCZero is reduced to pawn moves, because moving its king or either rook leads to immediate disaster. The game continued until checkmate, per TCEC tournament rules.

208b6 209axb6 a5 210Qf7+ Rxf7 211gxf7+ Kf8 212fxe8+ Kxe8 213b7 Kf8 214Kf6 Kg8 215b8(Q)+ Kh7 217Qc7+ Kg8 218Qg8 checkmate.

(Stockfish-LCZero Final Position)

To view this game on a virtual board, go to https://chess24.com/en/watch/live-tournaments/tcec-season-21-superfinal-2021/1/1/68.

Keith Holzmueller has been the head coach of the Evanston Township High School Chess Club and Team since 2017. He became a serious chess player during his high school years. As an adult player, he obtained a US Chess Federation Expert rating for over-the-board play and wasawarded the Senior International Master title by the International Correspondence Chess Federation. Keith now puts most of his chess energy into helping young chess players in Evanston learn to enjoy chess and improve their play.Please email Keith at news@evanstonroundtable.com if you have any chess questions.

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Rank and File | Artificial intelligence comes to the fore in computer chess - Evanston RoundTable

Sep. 10, 2021Updated: Sep. 10, 2021 2:41p.m.

An IBM researcher holds a silicon wafer with embedded IBM Telum chips designed to maximize artificial intelligence capabilities. The chips were developed at Albany Nanotech and made in partnership with Samsung. The Albany area was recent cited by the Brookings Institution for having the potential to create an AI sector.

ALBANY The Capital Region is one of 87 "potential adoption centers" in the United States for companies and researchers focused on the use of artificial intelligence, or AI, according to a new report from the Brookings Institution, a left-leaning think tank. The San Francisco Bay area is No. 1 in AI, while other upstate cities, Buffalo, Rochester and Syracuse, were also listed as potential adoption centers.

The Center for Economic Growth in Albany highlighted the Brookings list as part of its own report recently published on AI research and development in the Capital Region at local universities and at companies such as IBM and General Electric.

Larry Rulison has been a reporter for the Albany Times Union since 2005. Larry's reporting for the Times Union has won several awards for business and investigative journalism from the New York State Associated Press Association and the New York News Publishers Association. Contact him at 518-454-5504 or lrulison@timesunion.com.

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Region's AI sector has potential according to think tank - Times Union

Arushi Kapoor

Many people considered it a formless blur of colors, an image that was abstract but slightly resembling a human face. The image isnt even properly positioned on the canvas, rather it is skewed towards the northwest.

In October 2018, this art piece: Portrait of Edmond de Belamy, an algorithm-generated print, was sold for $432,500, thus beginning the AI-Art goldRush.

Humans have always created and enjoyed all forms of art, for viewing purposes, for aesthetic purposes, and even for therapeutic purposes. Since the discoveries of an artistic shell carved by homoerectus, the art business has grown in leaps and bounds and become a highly profitable industry. Leonardo Davincis, Salvator Mundi went for $450.3 million, becoming the most expensive art piece to date.

Understanding and thriving in this industry is not as easy as it may appear, it requires a lot of knowledge, time, and exposure. 25-year-old Arushi Kapoor is the CEO and co-founder of ARTSop art consulting, is an entrepreneur who boasts all of these traits. She is also the founder of Arushi, a cultural center and art warehouse based in Echo Park, Los Angeles.In this article, Kapoor shares her knowledge of the art industry and the influence that tech and AI have on it.

Technology has impacted the way art is created and enjoyed for the better part of the last 100 years, the invention of portable paint tubes enabled artists to paint outdoors and sparked a contingent of stunning landscape and horizon paintings. Today cameras and software like Photoshop have redefined the way art is created and enjoyed.

Kapoor, who is herself a tech-enthusiast agrees that these advancements have been great, but insists that they have not changed the antiquated meaning of art.

I will always be grateful for technology and technological advancements, says Kapoor.I wouldnt have a business or be able to do what I have done in the industry since the age of 19, had it not been for technologies of various kinds.

She continues,However, in my experience, I feel that there is still and will always be that reverence in the hearts of art lovers towards handmade art and crafts. Technological creations have great utility and aesthetic value, but paintings and craft tend to have what I refer to as artistic glory. Human creativity is what art is all about. Technology is a help to it, not a full replacement for it.

Kapoors foray into the industry dates back to when she wrote her first book, Talking Art at age 19. With that book, she put the world on notice that art was not going to be just a fleeting interest for her. Kapoor grew up in India, Europe, and the US, and this multicultural exposure has certainly influenced her knowledge and understanding of art.

Kapoor is the director of Arushi, a US-based venture that made history as the first to present a sold-out all-Indian art show; Art of India, Reclaiming The Present.

ArtSop Consulting, a facet of Arushi, provides private art consulting to people around the world, buying and selling art for clients in the secondary art market. Additionally, ArtSop represents primary artists that are featured in the art warehouse, Arushi.

Kapoor is also a technology investor, who has done a lot of research and invested capital into AI-driven art startups that are moving the needle when it comes to the future of art tech.

Kapoor comments that the integration of AI and art has been received with mixed feelings.

Personally, I havent seen any extraordinary artworks created by AI exclusively yet, she says. I think there is always going to be some human intervention required to create out of the park art. I recently heard, DeviantArt is an AI tool thats helping find stolen artworks. Thats extraordinary and thats how I believe AI can make a positive impact on the art world

The success of the AI-generated Portrait of Edmond de Belamy seems to have sparked off a series of AI art creations all wanting to cash out on the AI intrigue among some high spending art lovers.

In a recent exhibition of prints shown at the HG Contemporary gallery in Chelsea, the epicenter of New Yorks contemporary art world, 20 prints were displayed as part of the Faceless Portraits Transcending Time.

The ARTSop CEO isnt necessarily intrigued by this development, Kapoors MO has always been about highlighting upcoming local and female contemporary artists who have no platform to showcase their creations. In the opening of her Invite-only warehouse in LA, she featured a local female artist, Lindsay Dawn, for her first exhibition. Kapoor believes that real art should be discovered and celebrated.

If AI prints continue to sell for huge amounts it may de-incentivize actual human creation and creativity, says Kapoor.

Arushi Kapoor

At the rate at which technology is being accepted in every industry, it is no longer difficult to imagine a future where fewer artists are creating because they lack platforms to sell. Arushi along with many other art companies and galleries, hopes to find a balance and to create an ecosystem where both kinds of art can co-exist in the future. This shift to accepting non man made artworks isnt widely accepted currently. I am optimistic that there would always be a large section of art lovers who prefer man-made creations or perhaps love both.

Artificial Intelligence wasnt initially applied to art as a creator but as an impersonator. The technique is called style transfer and it uses deep neural networks to replicate, recreate and blend styles of artwork, by teaching the AI to understand existing pieces of art. Alexandra Squire is an excellent example of how the very human process of making art is not easily replicated. Squire believes art is a universal language with vast meanings, and focuses on art that is substantial, open to interpretation, and rich in depth and texture.

The increased usage of all kinds of AI in all kinds of art suggests that it is here to stay. From the AI-written book, 1 The Road, to Anna Riddlers AI-generated blooming tulip videos, creators have found value in utilizing artificial intelligence.

The question then becomes, is AI the future of the art industry? Kapoor shares her sentiment on this pertinent question.

Kapoor adds, The more optimistic view is that artificial intelligence evolves into a greater tool for existing creators to enhance, discover and replicate their works. We all hope for a world where our technologies help us, and not replace us.

Kapoors perspective on the future of art and AI is probably the most tenable and desirable. There is a strong perception amongst art lovers that machines can not produce art in the real sense of the word.

This sentiment is partly true because so far, AI has only demonstrated an ability to study and understand existing art and to somehow enhance or combine them to produce something new, and in some cases, something better.

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Is Artificial Intelligence Set To Take Over The Art Industry? - Forbes

If you want to imagine the future of healthcare, you can do no better than to read cardiologist and bestselling author Eric Topols trilogy on the subject: The Creative Destruction of Medicine, The Patient Will See You Now, and Deep Medicine.

Deep Medicine bears a paradoxical subtitle: How Artificial Intelligence Can Make Healthcare Human Again. The book describes the growing interaction of human and machine brains. Topol envisions a symbiosis, with people and machines working together to assist patients in ways that neither can do alone. In the process, healthcare providers will shed some of the mind-numbing rote tasks they endure today, giving them more time to focus on patients.

I recorded an interview with Topol in which we discuss his books. The podcast is titled Healthcares Reluctant Revolution because one of Topols themes is that healthcare is moving too slowly to integrate AI and machine learning (ML) into medicinea sluggishness that diminishes the quality and quantity of available care.

The first of Topols books, Creative Destruction, described how technology would transform medicine by digitizing data on individual human beings in great detail. In The Patient Will See You Now, he explored how this digital revolution can allow patients to take greater control over their own health and their own care. With this democratization of care, medicines ancient paternalism could fade. (In 2017, Topol and I co-authored an essay on Anatomy and Atrophy of Medical Paternalism.)

Deep Medicine is qualitatively different from the other two books. It has an almost-mystical quality. Intelligent machines engaging in AI and ML arrive at information in ways even their programmers can barely comprehend, if at all. Topol gives a striking example.

Take retinal scans of a large number of peoplethe sort of scans that your optometrist or ophthalmologist takes. Now, show the scans to the top ophthalmologists in the world and ask for each scan, Is this person a man or a woman? The doctors will answer correctly approximately 50 percent of the time. In other words, they have no idea and could do just as well by tossing a coin. Now, run those same scans through a deep neural network (a type of AI/ML system). The machine will answer correctly around 97 percent of the timefor no known reason.

Topol explains how such technologies can improve care. Today, radiologists spend their days intuitively searching for patterns in x-rays, CT scans, and MRIs. In the future, much of the pattern-searching will be automated (and more accurate), and radiologists (who seldom interact with patients today) will have much greater contact with patients.

Today, dermatologists are relatively few in number, so much of the earlier stages of skin care are done by primary care physicians, who have less ability to determine, say, whether a mole is potentially cancerous. The result can be misdiagnosis, delayed diagnosis, and the unnecessary use of dermatologists time. In the future, primary care doctors will likely screen patients using smart diagnostic tools, thereby wasting less of patients and dermatologists time and diagnosing more accurately.

In Deep Medicine, Topol tells the story of a newborn experiencing seizures that could lead to brain damage or death. Routine diagnostics and medications werent helping. Then, a blood sample was sent to a genomics institute that combed through a vast amount of data in a short time and identified a rare genetic disorder thats treatable through dietary restrictions and vitamins. The child went home, seizure-free, in 36 hours.

Unfortunately, healthcares adoption of such technologies is unduly slow. In our conversation, Topol noted that we have around 150 medical schools, some quite new, and yet they dont have any AI or genomics essentially in their curriculum.

Topol lists some hopes that observers invest in AI: Machines outperforming doctors at all tasks, diagnosing the undiagnosable, treating the untreatable, seeing the unseeable on scans, predicting the unpredictable, classifying the unclassifiable, eliminating workflow inefficiencies, eliminating patient harm, curing cancer, and more.

A realistic sort of optimist, Topol writes: Over time, AI will help propel us toward each of these objectives, but its going to be a marathon without a finish line.

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Artificial Intelligence and the Humanization of Medicine InsideSources - InsideSources

Distribution centers provide a controlled environment that is ideal for testing and proving complex technologies like drones and robots. That's also one reason why DCs are experimenting heavily with Artificial Intelligence (AI).

An independent research survey commissioned by Lucas Systems found that the majority of companies are already using AI in their warehouses and distribution/fulfillment operations. The survey also revealed that operators view cost, complexity, and lack of understanding of how to use AI as key impediments to further investments.

In reality, AI will make it easier and less costly for DCs of all sizes to address warehouse optimization challenges like slotting and workforce planning. And successful use of AI will not require massive investments in data science departments. Here's why.

Good data is a key to effective AI, and DCs are a good environment for collecting and aggregating historical and real-time data. AI is also a natural fit for DC operational challenges that previously required highly-engineered expert systems that are costly to implement and maintain.

AI and machine learning-based solutions reduce those obstacles, and they give DCs better results than current resource and inventory management approaches that rely on Excel, inherited best practices, or simple rules-based decision-making. AI is making advanced optimization practical for smaller operations, and more flexible and cost-effective for larger facilities.

Lucas has identified five key applications for AI in the warehouse today.

Proper product slotting impacts labor productivity, throughput, and accuracy, but doing it well isn't easy. Slotting is both a combinatorial optimization problem (many input factors to consider) and a multiple objective optimization problem (with many goals, sometimes competing). In addition, there are thousands of products and product locations (slots) to consider, and those products and locations may change frequently.Traditional slotting solutions require customized models and extensive engineering, measurement and data collection, both to install and maintain.

AI eliminates much of the engineering work and manual warehouse mapping and data inputs required for traditional slotting systems. AI-based software can learn the spatial characteristics and travel time predictions required for a slotting model based on activity-level data captured in the DC. And the learned model will adapt as conditions change, providing continuous optimization.

Optimal labor allocation is essential to ensuring orders get out on time while eliminating overstaffing and understaffing. In many DCs, supervisors make staff allocation decisions throughout a shift based on the volume of work, deadlines, and current and expected productivity. Good decisions require good data and accurate predictions, which today are often based on each manager's individual experience and skill.

To improve results, machine learning can be applied to predict labor requirements and work completion times. An AI solution can also run simulations to determine how to best complete the work, avoiding delays and ensuring the most efficient use of labor.

Labor management systems using Engineered Labor Standards (ELS) have been around for years. AI can eliminate much of the labor-intensive data collection process required with ELS-based performance management, using learning algorithms to predict the time required to complete tasks.

AI algorithms learn based on real-world performance data collected from within the operation, taking into account a multitude of variables (user, work type, work area, starting travel location, ending travel location, product to be handled, quantity to be handled, etc.).The predicted results and expectations are more accurate and the ML models adjust when operational changes are introduced.

Warehouse workers spend much of their workday traveling within a facility, making travel reduction a key to improved productivity. Automation and robots each eliminate travel, and AI can be used in areas where automation alone is not enough.

AI and machine learning systems use large amounts of process data to 'learn'how to balance priorities and reduce travel through intelligent order batching and pick sequencing. The systems take into account common congestion areas and slow-moving routes. Many DCs have achieved 2x productivity gains in piece picking applications using AI-based travel reduction, and even case pick to pallet operations have demonstrated 20-30 percent productivity gains.

The same tools used to optimize travel for workers can apply to orchestrating people and autonomous mobile robots (AMRs) in an order-picking process. In most pick-to-robot systems today, the robot system optimizes and directs the robots to a location, and a nearby worker delivers one or more picks to the robot based on instructions on a tablet mounted to the machine.

An AI-based execution system can orchestrate and optimize for both the robots'and the pickers'time while also providing means to direct workers independent of the AMRs (using wearable mobile devices rather than robot-mounted tablets).Machine learning algorithms predict where the robots and pickers will be located at a given time, and other algorithms provide input to intelligently organize and sequence the work among people and robots.

In the survey mentioned earlier, the cost was seen as the biggest impediment to AI adoption, and 8 in 10 of the respondents also said their organizations need a better understanding of how AI can be used in the DC.

As outlined above, AI has the potential to reduce the cost and manual engineering time and effort required to implement a range of DC optimization solutions, from slotting to labor performance management. What's more, these new AI-based solutions do not require that companies develop extensive in-house AI expertise.

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5 applications for artificial intelligence in the warehouse and distribution center - Supply Chain Dive