Monthly Archives: February 2022

TEL-AVIV, Israel, Feb. 28, 2022 /PRNewswire/ -- Gefen's AI engine (GFN: ASX), the GQL (Genetically Qualitative Learner) merges data, expert intuition and machine capabilities. It takes the experience and expertise of the most capable and experienced agents, managers and innovators in its arena and makes it available over big data analysis, digital channels and at large scale.

An example GQL will harness Open Insurance (which policies a customer has from all vendors), customer data (age, income, family status) and expert knowhow (what should a clever agent do for the benefit of all parties involved) and will recommend which policies should be upgraded, replaced or even canceled. The GQL takes into account thousands of products, customer actions, interactions and data points and optimizes the recommendations for the benefits of customers, agents, advisors and vendors.

Gefen is now releasing the next generation of GQL with more capabilities, more data points and new targeting to interactions between agents, advisors and customers. For example - GQLs can now be specifically aimed towards improving work day priorities (which customer to contact, how and when), in-context recommendations (what to offer a customer during a call) and which customers to target with which digital content.

The GQL's main purpose is to improve the level of service, the personalized fit for the customers and increase the share of wallet generated from each customer.

The new GQL generation is being gradually deployed and will be made available to the entire platform during the first quarter of 2022.

For further information, please contact:

Investor & Media EnquiriesGefen International AI LTDOrni Daniels, Co-CEO[emailprotected]

SOURCE Gefen International

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Gefen is releasing the next generation of its proprietary AI engine (GQL) - PRNewswire

In the age of AI, the weapons of war have become more technologically advanced than at any time in history. Russias military, as its incursion into Ukraine continues, is no exception.

Russia is one of the biggest spenders on defense in the world, with World Bank figures pegging it at $62 billion in 2020, eclipsed only by the U.S. and China. All three nations have been testing a plethora of AI units and weapons.

As far back as 2017, President Vladimir Putin had said that whoever becomes the leader [in AI] will become the ruler of the world.

Here is a snapshot of Russias AI arsenal, some of which are being used against Ukraine.

Like most militarized modern nations, Russia has fleets of drones.

Its KUB-BLA drone was developed by the Kalashnikov Group, the same company that produces Russias iconic assault rifles. Designed to destroy remote ground targets, it delivers payloads onto a targets coordinates that are set manually or in the image from the drones guidance system.

Russia has deployed its drones in combat prior to its invasion of Ukraine. Its military has been intervening in the Syrian civil war at the request of the Syrian government for military aid against rebel groups.

Russias Khmeimim base houses its Syrian drone operations, as well as radar and surveillance equipment. It has been targeting militants in places like Idlib using suicide drones autonomously targeting UAVs (unmanned aerial vehicles).

Its KYB-UAV drones developed by ZALA Aero, a Kalashnikov subsidiary, self-destruct when striking its target. Russian Defence Ministry initially tested the drones in Syria in late December and planned to widen their use in 2022.

AI Weapons: Autonomous Combat Vehicles and AI-Guided Missiles

In terms of autonomous weapons, Russia has deployed unmanned ground vehicles for tasks ranging from bomb disposal to anti-aircraft, and of course, killing.

Its Uran-9 UCGV (unmanned combat ground vehicle) was developed by JSC 766 UPTK, also part of the Kalashnikov group. It saw deployment on the ground in Syria was used in the large-scale Vostok drills in 2018. It is fitted with a 30mm 2A72 autocannon, as well as 4 9M120 Ataka anti-tank missiles and up to 12 Shmel-M thermobaric rocket launchers.

The autonomous units were part of further large-scale tests late last year, with Russian armed forces chief General Oleg Salyukov confirming the Uran-9 would be accepted into service by the Russian Ground Forces during 2022 for both combat and reconnaissance purposes, according to military publication Janes.

On the oceans, it has plans to incorporate AI into crewless naval and undersea vehicles. Last November, the Russian Ministry of Defence reportedly was arming naval vessels with Kamikaze drones to strike ground targets, enemy ships and aid special forces soldiers performing secret missions.

And in the air, Russia reportedly has been working on developing AI-guided missiles that could decide to switch targets mid-flight since at least early 2017, emulating advanced technology in the U.S. Raytheon Block IV Tomahawk cruise missile.

This article first appeared in IoT World Todays sister publication AI Business.

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Russia's AI Army: Drones, AI-Guided Missiles and Autonomous Tanks - IoT World Today

The Gran Turismo Sophy A.I. does a lap of the course. Sony A.I. hide caption

The Gran Turismo Sophy A.I. does a lap of the course.

An artificial intelligence program has beaten the world's best players in the popular PlayStation racing game Gran Turismo Sport, and in doing so may have contributed towards designing better autonomous vehicles in the real world, according to one expert.

The latest development comes after an interesting couple of decades for A.I. playing games.

It began with chess, when world champion Garry Kasparov lost to IBM's Deep Blue in a match in 1997. Then with Go, when A.I. beat Korean champion Lee Sedol in 2016. And by 2019, an A.I. program ranked higher than 99.8% of world players in the wildly popular real-time strategy game StarCraft 2.

Now, an A.I. program has dethroned the best human players in the professional esports world of Gran Turismo Sport.

In a paper published recently in the science journal Nature, researchers at a team led by Sony A.I. detailed how they created a program called Gran Turismo Sophy, which was able to win a race in Tokyo last October.

The world's best human Gran Turismo players compete against GT Sophy. Sony A.I. hide caption

The world's best human Gran Turismo players compete against GT Sophy.

Peter Wurman is the head of the team on the GT Sophy project and said they didn't manually program the A.I. to be good at racing. Instead, they trained it on race after race, running multiple simulations of the game using a computer system connected to roughly 1,000 PlayStation 4 consoles.

"It doesn't know what any of its controls do," Wurman said. "And through trial and error, it learns that the accelerator makes it go forward and the steering wheel turns left and right ... and if it's doing the right thing by going forward, then it gets a little bit of a reward."

"It takes about an hour for the agent to learn to drive around a track. It takes about four hours to become about as good as the average human driver. And it takes 24 to 48 hours to be as good as the top 1% of the drivers who play the game."

And after another 10 days, it can finally run toe-to-toe with the very best humanity has to offer.

After finishing behind two bots controlled by Gran Turismo Sophy at the race in Tokyo, champion player Takuma Miyazono said it was actually a rewarding experience.

Driver Takuma Miyazono races against Gran Turismo Sophy. Sony A.I. hide caption

Driver Takuma Miyazono races against Gran Turismo Sophy.

"I learned a lot from the A.I. agent," Miyazono said. "In order to drive faster, the A.I. drives in a way that we would have never come up with, which actually made sense when I saw its maneuvers."

Chris Gerdes is a professor of mechanical engineering at Stanford and reviewed the team's findings through its publication process at Nature. Gerdes also specializes in physics and drives race cars himself.

He said he spent a lot of time watching GT Sophy in action, trying to figure out if the A.I. was actually doing something intelligent or just learning a faster path around the same track through repetition.

"And it turns out that Sophy actually is doing things that race car drivers would consider to be very intelligent, making maneuvers that it would take a human race car driver a career to be able to pull some off ... out of their repertoire at just the right moment," he said.

What's more, Gerdes said this work could have even greater implications.

"I think you can take the lessons that you learned from Sophy and think about how those work into the development, for instance, of autonomous vehicles," he said.

Gerdes should know: He researches and designs autonomous vehicles.

"It's not as if you can simply take the results of this paper and say, 'Great, I'm going to try it on an autonomous vehicle tomorrow,'" Gerdes said. "But I really do think it's an eye opener for people who develop autonomous vehicles to just sit back and say, well, maybe we need to keep an open mind about the extent of possibilities here with A.I. and neural networks."

Wurman and Gerdes both said that taking this work to cars in the real world could still be a long way off.

But in the short term, Wurman's team is working with the developers of Gran Turismo to create a more engaging A.I. for normal players to race against in the next game in the series.

So in the near future, we could try our hands at racing it, too.

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AI mastered PlayStation's 'Gran Turismo' video game, but it could have more uses - NPR

It is no secret that many enterprises and organizations, and procurement chain managers within them, had to change their worldviews during the last two years and produce new and agile responses and action plans to meet their business objectives.

The current and visible reality will continue requiring adjustments in work processes, tools, and how professional elements get ready to answer needs and challenges, alongside continuing uncertainty and fluctuations in internal and external environments.

The dynamic and fragile state of the differfent markets, including failure of suppliers to meet delivery deadlines; critical shortage of raw materials, components, and products; significant price fluctuations; all these, and more, impact the enterprises' ability to meet demands and remain profitable, demanding course recalculation while understanding that the old and familiar trajectory of procurement processes and supplier relations must change.

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Boaz Gilad, CEO of BizWatch

(Alan Chapelski)

In the resulting complex, even chaotic state, enterprises have become more and more dependable on the suppliers' performance quality. This situation has caused many enterprises to tighten their connections with the existing suppliers if it decreases risks and prevents compromising the continuity of activities. Additionally, enterprises increased the safety stock in their warehouses to avoid a shortage of raw materials and/or components. Seemingly, this is the natural action taken by elements that recognize the existing supplier's quality, the complexity of the market, and the difficulty of sourcing and setting up a new supplier in organizational procurement systems.

But this is precisely the essence of the problem. By doing so, enterprises deepen their dependence on existing suppliers, diminishing their flexibility and their business latitude, consecutively finding themselves constrained against their interests by a reduced number of suppliers.

That is not to doubt the suppliers' quality, reliability, and integrity for the record. But in a competitive business environment, we must always be one step ahead, understand the challenges facing us and prepare accordingly.

In addition to analyzing and understanding current and future challenges, preparedness with suitable tools, methods, people, and processes is required to enable an appropriate response.

Due to the market situation during the last two years, procurement professionals found themselves increasingly busier, trying to source and analyze potential quality suppliers. Still, this is an almost impossible task without end-to-end advanced and supportive technology.

Since before the Coronavirus pandemic and more intensely after its onset, there has been a significant change in trade, especially in B2B transactions. Social and travel restrictions, along with social distancing, unprecedentedly catalyzed the development and integration of advanced technological systems and tools in procurement processes. Market analysis indicates that even now, enterprises pay over $15 billion dollars yearly for systems supporting procurement processes, especially for data analysis, risk analysis, CRM systems, and more. It is estimated that this scope will keep increasing because of the inherent advantages and challenges facing elements dealing with procurement, export, and import of products and raw materials. However, it must be noted that real change is not necessarily in the capacity for data processing (BI) of procurement processes, as was common until now.

The real revolution and disruption of the existing state are to be found in the combination of the ability to collect and process up-to-date, dynamic, and objective data from multiple data sources throughout the web and the addition of AI-based capabilities in procurement processes. The correct integration of advanced technologies provides the benefit of numerous advantages and capabilities, such as collecting extensive up-to-date data about market trends on the one hand and analyzing needs and demand on the other hand. Technology integration facilitates the automatic, efficient, fast, and advanced location and analysis of potential, previously unknown suppliers, fast and automated application for quotes or raw material data, prices, stocks, and availabilities, automatically analyzing the answers, thus facilitating procurement elements with the ability to function in digital trade arenas against a broad and dynamic range of quality suppliers.

Only organizations that adopt the required agility will withstand the storm and see it as an opportunity and leverage for fulfillment, development, and growth. In the last two years, CEOs, supply chain VPs and procurement managers have increasingly realized that the entire chain of value must go through digital upgrade and transformation since an upgrade of core production processes alone isn't sufficient, and they must bring supporting processes to the fore of technology, i.e., procurement.

This would allow procurement professionals to be proactive, manage and support production processes out of maximal flexibility, systematic data-based view, optimization, and implementation of a new automation-based procurement process out of a dynamic mix of quality suppliers

Boaz Gilad is the founder and CEO of BizWatch LTD, a portfolio member of i4Valley

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The significance of AI-integrated procurement in an era of uncertainty | Ctech - CTech

SANTA MONICA, Calif.--(BUSINESS WIRE)--Dr.Evidence, the leading medical intelligence platform for life sciences companies, today announced groundbreaking enhancements to their market-leading medical search solution, DocSearch. DocSearch has expanded its coverage to include global patents and grants to its database and included the ability to identify new connections and generate hypotheses based on published literature with a Drugs-Targets module.

DocSearch is a specialized, real-time medical search engine powered by AI that generates actionable insights based on the universe of published medical information, real-world evidence and proprietary data. With the addition of the new Drugs-Targets module, it becomes possible to get more from published literature by establishing links between chemicals, targets, and disease, helping identify previously unrealized connections. This literature-based discovery approach creates the opportunity for researchers to explore a wide range of new hypotheses.

This expanded functionality drives potential breakthroughs by connecting existing knowledge in literature in new ways, said Dr.Evidences Head of Search, Arturo Devesa. Using the principle of Swanson Linking, we can uncover previously hidden connections between supposedly unrelated concepts in the biomedical literature and see possibilities emerging, leading to new hypotheses, in-silico drug-target profiling, target identification search, and disease-drug-target links and associations discovery search.

In addition to the Drugs-Targets module, DocSearch has added global patents (25 million) and grants (600,000) to its database. The addition of these new data sources will support more robust, comprehensive searches and will be instrumental in an upcoming DocSearch feature, Literature Lifecycle Visualization. The visualization will enable users to track a drug or treatments end-to-end literature life cycle from grant to clinical trial to PubMed publication to patent to FDA label to congresses to news and social media.

Dr.Evidences Chief Executive Officer, Bob Battista, commented, The significant expansion of DocSearch capabilities and data sources is in direct response to the needs of our life sciences clients. We are committed to rapidly advancing the Dr.Evidence platform to positively impact patients lives by uncovering insights that drive innovation.

About Dr.Evidence

Dr.Evidence is the leading medical intelligence platform for life sciences companies that enables teams to identify breakthrough insights grounded in the vast universe of published medical information, real-world evidence and proprietary data. It pushes the boundaries of healthcare technology and allows for new possibilities in science, enabling more informed decision making and faster time-to-market for accelerated impact.

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Dr.Evidence Expands Capabilities of AI-powered Medical Search Engine - Business Wire

Art needs an audience, and their tastes vary. Computer programmers have not made art. They have made objects, media, performance, software work. What makes art is an audience as it is an artist who does work, and it is the audiences who turn that work into art through appreciation, says Mike Rugnetta of PBS Idea Channel.

AI art can be made using Generative Adversarial Networks (GAN), where two Artificial Neural Networks (ANN) are trained at the time in different ways to generate AI art. Now, developers have built various tools that help you create AI art easily. Most of these tools comply with the basic grounds of GAN with a few variations of their own.

So, what is AI Art?

Artificial intelligence art, or AI art, is any artwork created with the assistance of AI. It can be created autonomously by AI systems or created in collaboration with humans and an AI system.

In 2018, British auction house Christies sold its first piece of computer-generated art, titled Portrait of Edmond Belamy, made by a French art collective named Obvious, sold for a whopping $ 432,500, about 45 times more than its estimated worth.

Portrait of Edmond Belamy

AI art has been creating a lot of buzz among the artists community, and whether art enthusiasts fancy it or not, it is here to stay and will evolve continuously. Here is a look at some of the online tools available to create mind-blowing AI artwork:

Magenta

Magenta is an open-source research project tool that trains ML models to generate AI art and music. It does so by manipulating source data like music and images. Magenta was started by the Google Brain team, which developed new deep learning and reinforcement learning algorithms for generating songs, images, drawings, and other materials. It is also exploring building smart tools and interfaces that allow artists and musicians to extend their processes using these models. Magenta uses TensorFlow and releases the models and tools in open-source on GitHub.

Google Deep Dream

Google engineer Alexander Mordvintsev created the AI art generator Deep Dream. This AI generator creates dream-like hallucinogenic pictures and uses a convolutional neural network to find and enhance patterns in images. Check out the experimental video of Deep Dream below.

Runway ML

Runway ML is an easy, code-free tool that makes it simple to experiment with ML models creatively. Primarily a video-photo editing and ML software for creatives, it can also create AI art and morph photos and videos.

Runway ML tool

Artbreeder

This AI art generator can create AI pictures by mixing two images. Formerly known as Ganbreeder, it is a collaborative, machine learning-based art website. Using the models StyleGAN and BigGAN, the app allows users to generate and modify images of faces, landscapes, and paintings, among other categories. Check out the video about Introduction to Artbreeder below:

Chimera Painter

Chimera painter is an AI art tool that changes a simple drawing and creates an impressive picture. The tool works by adding features and textures to a drawing by giving it a realistic look. Though aimed at game developers, this GoogleAI powered tool can be used by anyone who wants to create a realistic-looking AI picture.

Chimera Painter

Fotor GoArt

Fotor GoArt is an AI art generator that can change any picture into stunning pieces of art in seconds without manual photo editing. Just upload your picture and choose a painting filter you like, and GoArt will automatically analyse and convert your original picture into a spectacular painting within seconds. GoArt gives an experience of a new innovative way of creating art.

Image: GoArt (Pinterest)

NVIDIA AI Playground

This AI playground is named after the post-Impressionist painter Paul Gauguin. It can create realistic landscapes and bring life to rivers, rocks and clouds using basic tools rendering high-quality AI art. The tools are easy to use and dont require expertise or background. Check out their GauGAN: Changing Sketches into Photorealistic Masterpieces video below:

AI Art Machine by Google Colab

This is an art machine where you can text and get AI art. This notebook is by Hillel Wayne based on the notebook by Katherine Crowson. The platform is simplified to make it more accessible for nonprogrammers.

There are learning videos about How to generate Mind-Blowing AI Art in 5 minutes for free on YouTube uploaded by Anant Vijay Soni from AVSTech and the A.I. Whisperer. Check out the video links below:

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Online tools to create mind-blowing AI art - Analytics India Magazine

Intel on Wednesday announcedthat it's updating its OpenVINO AI developer toolkit, enabling developers to use it to bring a wider range of intelligent applications to the edge. Launched in 2018 with a focus on computer vision, OpenVINO now supports a broader range of deep learning models, which means adding support for audio and natural language processing use cases.

"With inference taking over as a critical workload at the edge, there's a much greater diversity of applications" under development, Adam Burns, Intel VP and GM of Internet of Things Group, said to ZDNet.

Since its launch, hundreds of thousands of developers have used OpenVINO to deploy AI workloads at the edge, according to Intel. A typical use case would be defect detection in a factory. Now, with broader model support, a manufacturer could use it to build a defect spotting system, plus a system to listen to a machine's motor for signs of failure.

Besides the expanded model support, the new version of OpenVINO offers more device portability choices besides the expanded model support with an updated and simplified API.

OpenVINO 2022.1 also includes a new automatic optimization process. The new capability auto-discovers the compute and accelerators on a given system and then dynamically load balances and increases AI parallelization based on memory and compute capacity.

"Developers create applications on different systems," Burns said. "We want developers to be able to develop right on their laptop and deploy to any system."

Intel customers already using OpenVINO include automakers like BMW and Audi; John Deere, which uses it for welding inspection; and companies making medical imaging equipment like Samsung, Siemens, Philips and GE. The software is easily deployed into Intel-based solutions -- which is a compelling selling point, given that most inference workloads already run on Intel hardware.

"We expect a lot more data to be stored and processed at the edge," Sachin Katti, CTO of Intel's Network and Edge Group, said to ZDNet. "One of the killer apps at the edge is going to be inference-driven intelligence and automation."

Ahead of this year's Mobile World Congress, Intel on Thursday also announced a new system-on-chip (SoC) designed for the software-defined network and edge. The new Xeon D processors (the D-2700 and D-1700) are built for demanding use cases, such as security appliances, enterprise routers and switches, cloud storage, wireless networks, AI inferencing and edge servers -- use cases where compute processing needs to happen close to where the data is generated. The chips deliver integrated AI and crypto acceleration, built-in Ethernet, support for time-coordinated computing and time-sensitive networking.

More than 70 companies are working with Intel on designs that utilize the Xeon D processors, including Cisco, Juniper Networks and Rakuten Symphony.

Intel also said Thursday that its next-gen Xeon Scalable platform, Sapphire Rapids, includes unique 5G-specific signal processing instruction enhancements to support RAN-specific signal processing. This will make it easier for Intel customers to deploy vRAN (virtual Radio Access Networks) in demanding environments.

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Intel expands AI developer toolkit to bring more intelligence to the edge - ZDNet