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The Intersection of Reinforcement Learning and Deep Learning – CityLife

Exploring the Synergy between Reinforcement Learning and Deep Learning in Modern AI Applications

The intersection of reinforcement learning and deep learning has emerged as a promising frontier in the field of artificial intelligence (AI). As researchers and engineers continue to push the boundaries of what AI can achieve, the combination of these two techniques has become increasingly important in developing cutting-edge applications. This article will explore the synergy between reinforcement learning and deep learning, and how their fusion is shaping the future of AI.

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, which it uses to adjust its actions to maximize the cumulative reward over time. This trial-and-error approach enables the agent to learn complex behaviors without explicit supervision, making it well-suited for tasks where the optimal solution is not known in advance.

Deep learning, on the other hand, is a subset of machine learning that focuses on neural networks with many layers. These deep neural networks are capable of learning hierarchical representations of data, enabling them to automatically extract useful features and make predictions based on raw input. This has led to breakthroughs in various domains, such as image recognition, natural language processing, and speech recognition.

The fusion of reinforcement learning and deep learning, known as deep reinforcement learning (DRL), has shown great potential in tackling complex problems that were previously considered intractable. One of the most notable successes of DRL is the development of AlphaGo, a computer program developed by DeepMind that defeated the world champion of the ancient board game Go. This achievement was considered a major milestone in AI, as Go is a highly complex game with more possible board configurations than there are atoms in the universe.

The key to AlphaGos success was the combination of deep learning for pattern recognition and reinforcement learning for decision-making. The deep neural networks were used to evaluate the potential outcomes of different moves, while the reinforcement learning algorithm guided the search for the best move by exploring and exploiting the game tree. This approach allowed AlphaGo to learn from both human expert games and self-play, ultimately mastering the game at a superhuman level.

The success of AlphaGo has inspired researchers to explore the potential of deep reinforcement learning in other domains. One promising area is robotics, where DRL can be used to teach robots to perform complex tasks, such as grasping objects, walking, or flying. By combining the ability of deep learning to process high-dimensional sensory data with the trial-and-error learning of reinforcement learning, robots can learn to navigate and interact with their environment in a more natural and efficient way.

Another exciting application of DRL is in the field of autonomous vehicles. By training self-driving cars using deep reinforcement learning, researchers hope to develop systems that can safely and efficiently navigate complex traffic scenarios. This approach has the potential to revolutionize transportation, reducing accidents and improving traffic flow.

In the healthcare sector, DRL is being explored for drug discovery and personalized medicine. By leveraging the power of deep learning to analyze large-scale biomedical data and reinforcement learning to optimize treatment strategies, researchers aim to develop AI systems that can assist in the discovery of new drugs and the customization of treatments for individual patients.

In conclusion, the intersection of reinforcement learning and deep learning is proving to be a fertile ground for innovation in AI. By combining the strengths of these two techniques, researchers are making significant strides in solving complex problems across various domains. As the synergy between reinforcement learning and deep learning continues to be explored, we can expect to see even more groundbreaking advancements in the field of artificial intelligence.

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The Intersection of Reinforcement Learning and Deep Learning - CityLife

Chinese AI Giant SenseTime Unveils USD559 Robot That Can Play … – Yicai Global

Yicai Global) June 15 -- SenseTime Group, a leading Chinese developer of artificial intelligence software, has unveiled a USD3,999 (USD559) AI-powered robot that can play Chinese board game Go.

SenseRobot uses robotic arms to play Go against people on a real board, Yicai Global learned at yesterday's launch event in Shanghai. It has several difficulty levels to meet different needs, from beginners to professionals.

In 2016, Google's AI lab DeepMind developed AlphaGo, a Go computer program that defeated world champion Lee Sedol, becoming a hot topic in the field of science and technology. But AlphaGo was only a prototype, not accessible to ordinary families.

SenseRobot managed to integrate huge computing power into a very small hardware, Shen Hui, dean of SenseTime's Innovative Engineering Institute, told Yicai Global.

Last August, SenseTime introduced the first generation of SenseRobot that can play Chinese chess. Compared with the previous generation, the newest Go robot optimized its algorithm and computing power that can be reinforced by the cloud server in games against professionals, Shen noted.

The advent of large language models has helped a lot in product iterations, and chatbot capabilities have been widely used in developing SenseRobot to improve its research and development performance and efficiency, Shen added.

After ChatGPT went viral, Chinese tech giants, including Baidu and Alibaba Group Holding, have launched similar products. In April, SenseTime released LLM SenseChat and SenseNova, an AI model training system.

SenseTime's shares [HK: 0020] were trading down 0.4 percent at HKD2.25 (29 US cents) as of 11.25 a.m. today. They have plunged nearly 41 percent since their listing in December 2021.

Editors: Dou Shicong, Futura Costaglione

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Chinese AI Giant SenseTime Unveils USD559 Robot That Can Play ... - Yicai Global

Cyber attacks on AI a problem for the future – Verdict

Although the use of artificial intelligence (AI) is accelerating in security, its increased use could actually invite AI cyber attacks (including adversarial attacks) in varied systems, devices and applications.

Recent advances around the improvement of algorithms (Googles AlphaGo, OpenAIs GPT-3), and increasing computing power have accelerated AI across a number of potential applications and use cases.

Use cases stem across Automotive (computer vision and conversational platforms), Consumer Electronics (implementing virtual assistants, authentication via facial recognition, i.e. Apples FaceID), and Ecommerce and Retail (voice-enabled shopping assistants, personalized shop). Accordingly, based on GlobalData forecasts, the total AI market is demonstrating strong growth (includes software, hardware, and services) and will be worth $383.3bn in 2030, having grown at a compound annual growth rate (CAGR) of 21.4% from $81.3bn in 2022. As a result many of these use cases will be across a number of consumer and business application settings.

AI within cybersecurity is very much talked about in how AI can be utilized to increase cyber resiliency, simplify processes, and perform human functions. AI together with Automation and Analytics enables managed security providers to ingest data from multiple feeds and react more quickly to real threats, and apply automation to incidence response in a broader way.

AI in cybersecurity is also seen to solve the problem in the long run of resourcing, by in the short term providing a stop gap by streamlining human functions across Security Operations Centers (SOCs) this could be through for example cybersecurity technology components covering Extended, Detection and Response (XDR) that detect sophisticated threats with AI; and Security Orchestration, Automation and Response (SOAR) platforms that utilize Machine Learning (ML) to provide incident handling guidance based on past actions and historical data.

On the flip side, the increased use of AI in all applications (including cybersecurity) increases the chances of attacks on the actual AI/ML models in varied systems, devices and applications. In addition, adversarial attacks on AI could cause models to misinterpret information. There are many use cases that this could occur, and examples include iPhones FaceID access feature that makes use of neural networks to recognize faces Here there is potential for attacks to happen through the AI models themselves and in bypassing the security layers.

Cybersecurity products where AI is implemented is also a target as AI in cybersecurity entails acquiring data sets over time which are vulnerable to attacks. Other examples include algorithm theft in autonomous vehicles, predictive maintenance algorithms in sectors like Oil &Gas and Utilities which could be subject to State Sponsored attacks, identification breaches in video surveillance, and medical misdiagnosis in Healthcare.

The discussion of countering attacks on AI will gain momentum over the next two years as AI use cases increase. Regulations around AI security will also drive momentum and frameworks in place to address cyber attacks on AI.

As an example, current regulatory examples at a vertical level include The European Telecommunications Standards Institute (ETSI) Industry Specification Group for Telecoms that is focusing on utilizing AI to enhance security and securing AI attacks.

The Financial sector as a whole is in its infancy in terms of setting and implementing AI regulatory frameworks. Though, there have been developments in Europe for example, and the European Commission published a comprehensive set of proposals for the AI Act. However, the security component is limited.

The lack of guidance and regulation currently leaves a number of vertical sectors like Finance and Utilities vulnerable.

However, as more AI regulatory frameworks in the context of security are introduced, this could pave way to the rise of managed services specifically at addressing attacks on AI service propositions could entail looking at risk management profiles and laying down security layers around vulnerability assessments, and better integration of MLOps and SIEM/SOAR environments.

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Cyber attacks on AI a problem for the future - Verdict

The shocking truth about Wikipedias Holocaust disinformation – Forward

Artistic rendering of an editor adding Holocaust distortions to wikipedia Photo by iStock/Creative Commons/Forward Montage

Shira Klein June 14, 2023

Manipulating Wikipedia is all the rage these days. Companies, governments and even presidential candidates reportedly do it.

Yet we sleep well at night because we trust Wikipedias editors will protect us from blatant disinformation. After all, there are 125,000 active editors on English Wikipedia, 460 administrators and a 12-member Arbitration Committee, often dubbed Wikipedias Supreme Court. Above these volunteers towers the Wikimedia Foundation, with its 700-strong staff. Together, it comprises an entire security system.

This month, we are seeing the system fail. And it is time for the Wikimedia Foundation to get involved.

My colleague and I recently exposed a persistent Holocaust disinformation campaign on English Wikipedia.

The study, which I published with Jan Grabowski from the University of Ottawa, examined two dozen Wikipedia articles on the Holocaust in Poland and over 300 back pages (including talk pages, noticeboards, and arbitration cases, spaces where editors decide what the rest of the world will accept as fact).

To our dismay, we found dozens of examples of Holocaust distortion which, taken together, advanced a Polish nationalist narrative, whitewashed the role of Polish society in the Holocaust and bolstered harmful stereotypes about Jews.

People who read these pages learned about Jews supposed complicity in their own catastrophe, gangs of Jewish collaborators aiding the Gestapo and Jews supporting the communists to betray Poles. A handful of distortions have been corrected since our publication, but many remain.

A fraction of it is true: There were scattered instances of Jewish collaboration in WWII, for example. But Wikipedia inflates their scale and prominence. In one article that remains gravely distorted, alleged Jewish collaboration with the Nazis takes up more space than the Ukrainian, Belorussian and ethnic German collaboration combined.

In one glaring hoax discovered by an Israeli reporter, Wikipedia claimed for 15 years that the Germans annihilated 200,000 non-Jewish Poles in a giant gas chamber in the middle of Warsaw.

Wikipedias ArbCom just released aruling responding to our study, sanctioning several editors. While this may seem promising, in fact, ArbComs actions should concern anyone who cares about disinformation.

The problem is not the individual arbitrators, nor even ArbCom as a whole; the committees mandate is to judge conduct, never content. This is a good policy. We wouldnt want arbitrators, who are anonymous volunteers with no expertise in any particular subject, to control content. Wikipedias strength lies in its enabling anyone to edit, democratizing knowledge like never before.

But this leaves a gaping hole in Wikipedias security apparatus. Its safeguards only protect us from fake information when enough editors reach a consensus that the information is indeed fake. When an area is dominated by a group of individuals pushing an erroneous point of view, then wrong information becomes the consensus.

Wikipedias structure leaves it vulnerable to be exploited by any small group of people willing to spend the time to control the content, whether they are from a government or a corporation or are simply ideologically driven private individuals.

In theory, anyone can edit Wikipedia; no editor has any ownership over any article. Yet over the years, anyone who tried to fix distortions related to Holocaust disinformation faced a team of fierce editors who guard old lies and produce new ones.

These few editors, with no evident ties to any government, sport playful pseudonyms, such as Piotrus (Little Peter in Polish) or Volunteer Marek. But they are a resilient team whose seniority and prolific editing across the encyclopedia give them high status in Wikipedias editorial community. Methodically and patiently, they go from article to article, removing and adding content until it aligns with a Polish nationalist worldview. They misrepresent sources, use unreliable sources, and push fringe points of view.

To be sure, Wikipedia has policies in place to prevent source misrepresentation, unreliable sources and fringe claims. If an editor commits these violations repeatedly, administrators and arbitrators can kick them out.

But administrators and arbitrators lack the expertise to recognize when a source has been misrepresented. Instead, they focus on editors interpersonal conduct. Editors who are uncivil, aggressive or long-winded find themselves sanctioned, while those who are polite and show a willingness to compromise generally emerge unscathed, regardless of the content they author.

This problem is not unique to Wikipedias treatment of the Holocaust. A similar disinformation campaign is taking place in Wikipedias articles on Native American history, where influential editors misrepresent sources to the effect of erasing Native history and whitewashing American settler colonial violence. The Wikipedia article on Andrew Jackson, plagued by such manipulations, attracts thousands of readers a day.

This was the third ArbCom case on the Holocaust to make the same mistakes. ArbCom paid lip service to the importance of tackling source manipulations, while completely disregarding dozens of such problems presented to them by our study and by concerned editors. By ignoring egregiously false content, and focusing only on editors civility, ArbCom sends the message that theres no problem with falsifying the past, as long as you are nice about it.

The results are tragic: The arbitrators have banned one editor who, as our article showed, had brought in trustworthy scholarship to rebut the distortions. They sanctioned another editor for documenting the distortionists whitewashing of current Polish antisemitic figurines (called, tellingly, Jew with a Coin).

Worse still, they have described as exemplary a distortionist editor who has defended Holocaust revisionist Ewa Kurek. Kurek has claimed that Jews had fun in the Warsaw ghetto and that COVID-19 is a Jewfication of Europe. Two additional editors who were banned are indeed distortionists, but the ban (appealable in 12 months) responded to their bad manners, not their manipulation of history.

The Wikimedia Foundation needs to intervene, as it has already done to stem disinformation in Chinese Wikipedia, Saudi Wikipedia and Croatian Wikipedia, with excellent results. It must do so in English Wikipedia as well.

In a statement they issued last week in response to press inquiries about our study and the recent ArbCom decision, the foundation said, Wikipedias volunteer editors act as a vigilant first line of defense.

But what is the second line of defense? What happens when cases keep bouncing back to ArbCom, as has occurred with the Holocaust in Poland, India-Pakistan, Armenia-Azerbaijan and gender and sexuality, to mention just a few controversies?

The Wikimedia Foundation must harness subject-matter experts to assist volunteer editors. In cases where Wikipedias internal measures fail repeatedly, the foundation should commission scholars mainstream scholars who are currently publishing in reputable peer-reviewed presses and work in universities unencumbered by state dictates to weigh in.

In the case of Wikipedias coverage of Holocaust history, there is a need for an advisory board of established historians who would be available to advise editors on a sources reliability, or help administrators understand whether a source has been misrepresented.

The foundation certainly has the resources to build more bridges with academia: It boasts an annual revenue of $155 million, mostly from the publics donations. The public deserves a Wikipedia that provides not just any knowledge, but accurate knowledge, and asking for academics help is a necessary next step in Wikipedias ongoing development.

This is no radical departure from Wikipedias ethos of democratized knowledge that anyone can edit. This is an additional safeguard to ensure Wikipedias existing content policies are actually upheld.

Academia must also play its part to keep Wikipedia accurate. Scholars should uncover Wikipedias weaknesses and flag them for editors to fix, instead of snubbing Wikipedia as unreliable. Wikipedia is the seventh-most-visited site on the internet, most peoples first and last stop for information. All the more so with ChatGPT, which amplifies online content to a deafening pitch.

Volunteer editors and professional experts need to work together to get it right.

To contact the author, email [emailprotected]

Shira Klein is an associate professor of history at Chapman University in California and co-author of the study, Wikipedias Intentional Distortion of the History of the Holocaust in The Journal of Holocaust Research.

The views and opinions expressed in this article are the authors own and do not necessarily reflect those of the Forward. Discover more perspective in Opinion.

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The shocking truth about Wikipedias Holocaust disinformation - Forward

New Mall-Based Minnesota Kohls Store Coming in 2024 | Joel … – NewsBreak Original

Despite a recent industry-wide department store downturn that fueled online speculation regarding the future of the company, the newest Kohls entity may not be the last.

This article is based on corporate postings and accredited media reports. Linked information within this article is attributed to the following outlets: Wikipedia.org, ScrapeHero.com, and EchoPress.com.

Wikipedia features a comprehensive and highly-attributed overview of the Kohls chain: Kohl's (stylized in all caps) is an American department store retail chain, operated by Kohl's Corporation... The company was founded by Polish immigrant Maxwell Kohl, who opened a corner grocery store in Milwaukee, Wisconsin, in 1927. It went on to become a successful chain in the local area, and in 1962 the company branched out by opening its first department store.

Regarding location count, ScrapeHero.com features the most recently-updated number: There are 1,157 Kohls retail stores in the United States as of December 11, 2022. The state with the most number of Kohls locations in the U.S. is California, with 117 retail stores, which is about 10% of all Kohls retail stores in the U.S.

It should be noted NewsBreak has published several articles of mine on the Kohls franchise, most detailing issues of a sale that was expected industry-wide in 2022 and the anticipated repercussions thereof.

That expected sale, however, did not happen.

See here for Plans For Kohls Closings in 2022, and here for Plans For Kohls Closings in 2022 Update: Sales Negotiations Terminated for further information.

Regardless, a new Kohls entity will be opening in 2024.

Let us explore.

According to a report from EchoPress.com, entitled Kohl's to Open in Alexandria's Viking Plaza Mall Next April, the identity of the malls new tenant had been closely guarded until recently.

As excerpted from the article: National retailer Kohls is coming to the west wing of the Viking Plaza Mall in 2024. The mall is owned by New Jersey-based company, Lexington Realty International , and managed by a regional team led by Nicki Martineau, Midwest Portfolio Manager in Minnesota. The Viking Plazas Senior Facility Manager is Sergio Rolfzen.

The new restaurant is reportedly scheduled to open next April.

The EchoPress.com piece goes on to state: The Viking Plaza Mall, along with Lexington Realty International, is excited to bring Kohls to the Alexandria community. We appreciate the hard work and collaboration of staff at Lexington, Viking Plaza Mall, Tradesmen Construction , and the City of Alexandria, Martineau said in a news release.

In the event of pertinent updates to these matters, I will share them here on NewsBreak.

Thank you for reading.

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New Mall-Based Minnesota Kohls Store Coming in 2024 | Joel ... - NewsBreak Original