Category Archives: Artificial Intelligence

Comprising discussion forums, interactive modules, industry networking and coursework, the Program's six courses will be delivered by instructors from world-leading academic institutions, executives from global multinationals, and the university's own faculty. These include Professor Eric Xing (President of MBZUAI); Professor Sir Michael Brady (Professor Emeritus, University of Oxford); Professor Daniela Rus (Director, MIT Computer Science and Artificial Intelligence Laboratory); Professor Michael Jordan (Pehong Chen Distinguished Professor, University of California, Berkeley); Professor Tom Mitchell (University Professor, Carnegie Mellon University); Dr. Kai-fu Lee (Chairman & CEO, Sinovation Ventures) and more.

H.E. Dr. Sultan Ahmed Al Jaber, Minister of Industry and Advanced Technology and Chairman of the MBZUAI Board of Trustees, said: "Following the establishment of the Mohamed bin Zayed University of Artificial Intelligence in 2019, today's unveiling of the MBZUAI Executive Program illustrates, once again, the determination of the United Arab Emirates to position itself at the forefront of the technologies and innovations shaping the global economy."

"Tailored to the needs of some of the UAE's most senior government and business executives, the MBZUAI Executive Program empowers decision makers in all industries to harness the benefits of AI in forging the future success of their respective organizations, in preparation for the nation's ambitions for the next 50 years."

Artificial intelligence is central to the UAE's national and economic growth agenda, with the potential to both unlock significant new growth from established industries and pave the way for entirely new business models and innovative technologies, H.E. Dr. Al Jaber added.

MBZUAI President Professor Eric Xing said: "Decision makers who understand AI-powered technologies and processes will be at the vanguard of sectors as diverse as healthcare, agriculture, energy, urbanization, transport, defense and more. To ensure that the UAE plays a leading role in shaping the industries of the future, it is imperative that our government and business decision makers actively engage with AI learning. I strongly believe that the MBZUAI Executive Program will give these leaders that competitive edge."

Its first cohort, of around 40 senior executives, will undergo 12 weeks of online practical courses, and seminars on the business, ethical and policymaking dimensions of the AI industry. The deadline for registration is October 7; while the first day of classes is October 23, 2021.

The Program's six courses include: An Introduction to AI; AI, Machine Learning and the Economy; Visual Cognition and Intelligence; Lingual Cognition and Intelligence; The Future of Robotics and AI Ethics and Policymaking.

Photo - https://mma.prnewswire.com/media/1609473/MBZUAI_Abu_Dhabi_UAE.jpgLogo - https://mma.prnewswire.com/media/1227419/MBZUAI_Logo.jpg

SOURCE Mohamed bin Zayed University of Artificial Intelligence

Read more:

Mohamed bin Zayed University of Artificial Intelligence launches Executive Program for UAE Government and Business Leaders - PRNewswire

As soon as Tom Smith got his hands on Codex a new artificial intelligence technology that writes its own computer programs he gave it a job interview.

He asked if it could tackle the coding challenges that programmers often face when interviewing for big-money jobs at Silicon Valley companies like Google and Facebook. Could it write a program that replaces all the spaces in a sentence with dashes? Even better, could it write one that identifies invalid ZIP codes?

It did both instantly, before completing several other tasks. These are problems that would be tough for a lot of humans to solve, myself included, and it would type out the response in two seconds, said Mr. Smith, a seasoned programmer who oversees an A.I. start-up called Gado Images. It was spooky to watch.

Codex seemed like a technology that would soon replace human workers. As Mr. Smith continued testing the system, he realized that its skills extended well beyond a knack for answering canned interview questions. It could even translate from one programming language to another.

Yet after several weeks working with this new technology, Mr. Smith believes it poses no threat to professional coders. In fact, like many other experts, he sees it as a tool that will end up boosting human productivity. It may even help a whole new generation of people learn the art of computers, by showing them how to write simple pieces a code, almost like a personal tutor.

This is a tool that can make a coders life a lot easier, Mr. Smith said.

About four years ago, researchers at labs like OpenAI started designing neural networks that analyzed enormous amounts of prose, including thousands of digital books, Wikipedia articles and all sorts of other text posted to the internet.

By pinpointing patterns in all that text, the networks learned to predict the next word in a sequence. When someone typed a few words into these universal language models, they could complete the thought with entire paragraphs. In this way, one system an OpenAI creation called GPT-3 could write its own Twitter posts, speeches, poetry and news articles.

Much to the surprise of even the researchers who built the system, it could even write its own computer programs, though they were short and simple. Apparently, it had learned from an untold number of programs posted to the internet. So OpenAI went a step further, training a new system Codex on an enormous array of both prose and code.

The result is a system that understands both prose and code to a point. You can ask, in plain English, for snow falling on a black background, and it will give you code that creates a virtual snowstorm. If you ask for a blue bouncing ball, it will give you that, too.

You can tell it to do something, and it will do it, said Ania Kubow, another programmer who has used the technology.

Codex can generate programs in 12 computer languages and even translate between them. But it often makes mistakes, and though its skills are impressive, it cant reason like a human. It can recognize or mimic what it has seen in the past, but it is not nimble enough to think on its own.

Sometimes, the programs generated by Codex do not run. Or they contain security flaws. Or they come nowhere close to what you want them to do. OpenAI estimates that Codex produces the right code 37 percent of the time.

When Mr. Smith used the system as part of a beta test program this summer, the code it produced was impressive. But sometimes, it worked only if he made a tiny change, like tweaking a command to suit his particular software setup or adding a digital code needed for access to the internet service it was trying to query.

In other words, Codex was truly useful only to an experienced programmer.

But it could help programmers do their everyday work a lot faster. It could help them find the basic building blocks they needed or point them toward new ideas. Using the technology, GitHub, a popular online service for programmers, now offers Co-pilot, a tool that suggests your next line of code, much the way autocomplete tools suggest the next word when you type texts or emails.

It is a way of getting code written without having to write as much code, said Jeremy Howard, who founded the artificial intelligence lab Fast.ai and helped create the language technology that OpenAIs work is based on. It is not always correct, but it is just close enough.

Mr. Howard and others believe Codex could also help novices learn to code. It is particularly good at generating simple programs from brief English descriptions. And it works in the other direction, too, by explaining complex code in plain English. Some, including Joel Hellermark, an entrepreneur in Sweden, are already trying to transform the system into a teaching tool.

The rest of the A.I. landscape looks similar. Robots are increasingly powerful. So are chatbots designed for online conversation. DeepMind, an A.I. lab in London, recently built a system that instantly identifies the shape of proteins in the human body, which is a key part of designing new medicines and vaccines. That task once took scientists days or even years. But those systems replace only a small part of what human experts can do.

In the few areas where new machines can instantly replace workers, they are typically in jobs the market is slow to fill. Robots, for instance, are increasingly useful inside shipping centers, which are expanding and struggling to find the workers needed to keep pace.

With his start-up, Gado Images, Mr. Smith set out to build a system that could automatically sort through the photo archives of newspapers and libraries, resurfacing forgotten images, automatically writing captions and tags and sharing the photos with other publications and businesses. But the technology could handle only part of the job.

It could sift through a vast photo archive faster than humans, identifying the kinds of images that might be useful and taking a stab at captions. But finding the best and most important photos and properly tagging them still required a seasoned archivist.

We thought these tools were going to completely remove the need for humans, but what we learned after many years was that this wasnt really possible you still needed a skilled human to review the output, Mr. Smith said. The technology gets things wrong. And it can be biased. You still need a person to review what it has done and decide what is good and what is not.

Codex extends what a machine can do, but it is another indication that the technology works best with humans at the controls.

A.I. is not playing out like anyone expected, said Greg Brockman, the chief technology officer of OpenAI. It felt like it was going to do this job and that job, and everyone was trying to figure out which one would go first. Instead, it is replacing no jobs. But it is taking away the drudge work from all of them at once.

Read more here:

A.I. Can Now Write Its Own Computer Code. Thats Good News for Humans. - The New York Times

AUSTIN, Texas Ethics will be at the forefront of robotics education thanks to a new University of Texas at Austin program that will train tomorrows technologists to understand the positive and potentially negative implications of their creations.

Today, much robotic technology is developed without considering its potentially harmful effects on society, including how these technologies can infringe on privacy or further economic inequity. The new UT Austin program will fill an important educational gap by prioritizing these issues in its curriculum.

In the next 10 years, we are going to live more closely alongside robots, and we want to be sure that those robots are fair, inclusive and free from bias, said Junfeng Jiao, associate professor in the School of Architecture and the program lead. And because the robots we create are reflections of ourselves, it is imperative that technologists receive an excellent ethics education. We want our students to work directly with companies to create practices and technologies that are equitable and fair.

Called CREATE (Convergent, Responsible, and Ethical AI Training Experience for Roboticists), it will offer graduate coursework and professional development in responsible design and implementation.

CREATE is a collaboration among Texas Robotics, industry partners and the UT grand challenge research initiative Good Systems, which seeks to design AI technologies that benefit society. The program has been recently awarded a $3 million grant from the National Science Foundation through its Research Traineeship Program, which will support 32 doctoral students to receive coursework, mentorship, professional development, internships, and research and public service opportunities.

Students will focus specifically on how to ethically design, develop and deploy service robots, which can make deliveries, work in factories and clean homes. They will consider factors such as how to design delivery service robots so they are more inclusive and can reach all people and how to ensure home service robots protect occupants privacy. Several notable robotics companies have also said they will offer students internships, including Sony AI, Bosch, Amazon, SparkCognition and Apptronik.

Researchers involved in the program cross many disciplines at UT, including computer science, architecture, engineering, information, and public affairs. Faculty members from these units will teach courses as part of the curriculum, and two faculty members will mentor each trainee during the five-year program. Additionally, each trainee will receive help with career development, grant writing, and exposure to local startup companies.

More than half of the programs trainees will be chosen from underrepresented groups in STEM education, including women and racial minorities, to help bring much-needed diversity to the field of robotics. The coursework component, which includes five classes in ethical robotics, will be institutionalized as a graduate portfolio program and will be available to all STEM graduate students at UT Austin.

This program will enable us to educate well-rounded roboticists who are not only grounded in the technical details of designing and building autonomous robots but also are equipped to fully consider the societal implications of their work, said Peter Stone, director of Texas Robotics and a professor of computer science. That is a missing part in robotics education in the U.S. and the world. We believe this is a game changer for the future of robotics.

Here is the original post:

Ethical Artificial Intelligence is Focus of New Robotics Program - UT News - UT News | The University of Texas at Austin

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.

Continued here:

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

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.

Here is the original post:

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

COPENHAGEN, Denmark--(BUSINESS WIRE)--Corti.ai, one of the leading SaaS companies in the fast-growing category of Artificial Intelligence for healthcare, announces a $27 Million Series A round.

The investment was led by Vaekstfonden -The Danish Growth Fund and Chr. Augustinus Fabrik, who joins existing investors Hearcore, Id Invest, and byFounders. The company was founded by Lars Maale and Andreas Cleve in 2016.

Unlike the majority of Artificial Intelligence startups that are pursuing image recognition use cases, Corti has focused on improving the workflow around patient consultations. Corti's machine learning platform can listen in during patient consultations and help to document, code, and quality assure the interaction in real-time, saving time and reducing risk. Corti started working within emergency medicine, supporting emergency calls focused on cardiac arrest and COVID-19 cases but has since then moved into supporting medical staff conducting consultations across healthcare.

Andreas Cleve CEO:Healthcare professionals only have a few minutes with each patient, and these encounters are compromised by keyboards and screens, hated by patients and doctors alike. What we've been able to prove at Corti is that machine learning can be a life-saving tool by offering a new kind of deep listening that will not only improve patient outcomes but also save time and money.

The company's patented technology automatically listens in during patient consultations on phone or video. Here it uses machine learning models to transcribe and analyze thousands of variables within each consultation.

Lars Maale CTO:"Not only is our technology able to document consultations, it automatically compares each patient's symptom description to millions of other patients to offer real-time decision support during the engagement, like nothing else available in the market today."

Since its inception several studies have validated the efficacy of Cortis human-computer partnership and found that Corti can help medical professionals deliver best-in-class results. Research from Copenhagen Emergency Services found that Corti could help reduce the amount of undetected out-of-hospital cardiac arrest cases by more than 40%, with almost no training of the personnel needed.

The new funding comes on top of the $5m seed round raised in 2019, and the company plans to use the money to fuel its expansion into primary care in the US. We will use this $27m raise to accelerate Cortis growth plans further in the coming years, new products will be launched, and we have plans to enter primary care and win the US market for consultations intelligence.

We are very proud of our work in the field of emergency medicine, but already today we are analyzing roughly 250.000 low acuity consultations per month, proving that the technology can be a massive value-add for both telehealth companies, clinical call-centers, and GPs around the world, Lars Maale explains.

The company has won several accolades for its innovations within applied artificial intelligence, including VentureBeat's Best Global AI Innovation 2018" and the "Future Unicorn Award 2020" award by the European Commission for being the most likely next unicorn from the European continent.

"Although we are humbled by the overwhelming feedback we have received, we have a dauntingly ambitious roadmap ahead, and as long as there are patients who need Corti to listen in to get the help they need, we have work to do'', CEO Andreas Cleve explains.

Several investors commented about their decision to back Corti.ai and its consultation intelligence platform:

"The global healthcare system has been tested over the last 18 months, and it has shown some fundamental challenges around availability and access to expertise that needs to be addressed. Robust and ethical technology can help solve some of these critical problems, and we believe Corti is a shining example of a revolutionary technology that can help define the market for artificial intelligence in healthcare.- Rolf Kjrsgaard, CEO Vaekstfonden

We have been following the company for a long time, and we are pleased to contribute to the continued rapid growth. To be able to build a category-defining product with world-class technology that can save human lives is not only commendable, it's also a fantastic opportunity that we are proud to back.- Claus Gregersen, CEO Chr. Augustinus Fabrikker:

About CortiCorti is a Danish health-tech company that has developed a software platform leveraging artificial intelligence to help healthcare personnel during patient consultations. As the consultation progresses, Corti's artificial intelligence is listening in to write notes, search databases, and compare symptom descriptions with millions of historical cases to ensure each patient gets the optimal treatment. The company is among the global leaders in applied artificial intelligence and has been recognized with several awards.

About Chr. Augustinus FabrikkerChr. Augustinus Fabrikker is a more than 270-year-old company that has positioned itself as a long-term and professional owner of Danish companies. This is done with great respect for the fact that ownership and day-to-day management are different disciplines. Chr. Augustinus Fabrikkers philosophy is that of being a committed and loyal owner with a focus on dialogue, trust, and long-term value creation in companies with an international outlook. The ownerships include Tivoli, Gyldendal, Jeudan, Royal Unibrew, Fritz Hansen and Podimo. Chr. Augustinus Fabrikker is a subsidiary of the Augustinus Foundation, which is one of Denmark's major cultural foundations. With a balance of more than DKK40 billion Chr. Augustinus Fabrikker's investments make it possible to reinvest actively in the Danish business community in parallel with contributing to the Augustinus Foundation's philanthropic activities. See more at: http://www.augustinusfabrikker.dk

About VaekstfondenVaeksfonden is the Danish states investment fund. Working in close collaboration with banks and domestic and international investors, Vaekstfonden discovers and develops the companies that Denmark cannot afford to miss out on. The power of innovation, yield to society and responsibility are the three signposts that guide Vaekstfonden in finding and choosing new projects. Since 1992, Vaekstfonden has contributed more than DKK 38 billion to help develop and grow more than 10.000 companies. See more on: https://vf.dk/.

Go here to read the rest:

Corti.ai Raises $27 Million in Series A Funding to Transform Patient Consultations With Artificial Intelligence - Business Wire

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.

See more here:

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

DUBLIN--(BUSINESS WIRE)--The "South America Workplace Services Market Forecast to 2028 - COVID-19 Impact and Regional Analysis By Service Type, Organization Size and Large Enterprises, and Vertical" report has been added to ResearchAndMarkets.com's offering.

Consumer Goods and Retail Segment is expected to be the fastest growing during the forecast period for the SAM region.

SAM Workplace Services Market is expected to reach US$ 7680.59 million by 2028 from US$ 3365.00 million in 2021. The market is estimated to grow at a CAGR of 12.5% from 2021 to 2028.

The report provides trends prevailing in the SAM workplace services market along with the drivers and restraints pertaining to the market growth. Rising significance of enterprise mobility is the major factor driving the growth of the SAM workplace services market. However, issues associated with the escalating security concerns hinder the growth of SAM workplace services market.

The market for SAM workplace services market is segmented into service type, organization size, vertical, and country. Based on services type, the market is segmented into end-user outsourcing services, and tech support services core. In 2020, the end-user outsourcing services segment held the largest share SAM workplace market.

Based on organization type the workplace services market is divided into- Small and medium-sized enterprises (SMEs) and large enterprises. Large enterprises is expected to the fastest growing segment over the forecast period. On the basis of vertical the market is segmented into Media and Entertainment, BFSI, Consumer Goods and Retail, Manufacturing, Healthcare and Life Sciences, Education, Telecom- IT and ITES, Energy and Utilities, Government and Public Sector, Others. The Telecom-IT and ITES segment accounts for largest market share in the 2020

The presence of various developing countries in SAM makes this region one of the key markets for the future growth of the workplace services market. The growing population, rising disposable income, high demand for advanced technologies, and huge focus on digital transformation are some of the key factors expected to drive the growth of the workplace services market in SAM.

The high number of confirmed cases and deaths due to COVID-19 in major SAM countries such as Brazil, Peru, Chile, Ecuador, and Argentina have affected the region in 2020. Subsequent to the coronavirus epidemic, IT and software businesses have received a lift in several countries of SAM since digital acceleration, and the need for remote work has accelerated the market growth even in the pandemic. Thus, the workplace services market is not majorly affected during the pandemic.

Accenture, Atos SE, Cognizant, Inc., Fujitsu Limited, HCL Technologies, IBM Corporation, NTT DATA Corporation, Tata Consultancy Services Limited, Unisys Corporation, and Wipro Limited are among some of the leading companies in the SAM workplace services market.

The companies are focused on adopting organic growth strategies such as product launches and expansions to sustain their position in the dynamic market. For instance, in 2020 Fujitsu announced it has signed a two-year agreement with HMRC, under the agreement Fujitsu will provide its Digital Workplace Services to HMRC.

Market Dynamics

Market Drivers

Market Restraints

Market Opportunities

Market Trend

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/rjhwae

Visit link:

South America Workplace Services Market Forecast to 2028: Unification of Artificial Intelligence (AI) to Revolutionize Workplace Services Business -...

By Shiran Weitzman, CEO, Shield

Compliance has always played a pivotal role across financial firms and banking institutions in an effort to pinpoint and mitigate various risks across communication channels, including market abuse, insider trading, spoofing, front-running, and even sexual harassment and racism. For decades, legacy vendors have been at the forefront of providing services to these institutions to flag and report any compliance and security breaches. This process was typically done manually where the compliance team would sift through emails and phone records and flag anything that appeared nefarious. While somewhat tedious, these compliance measures have helped institutions maintain industry wide standards and regulations as well as their own internal standards for employee conduct.

However, over time technology has evolved and has now become a mainstay across all industries, including banking and finance, offering support and assistance to organizations and institutions, making them more effective and efficient in their daily operations. More specifically, artificial intelligence has provided the banking and finance industries with comprehensive compliance tools that automate the process. While these measures can be implemented to make compliance and security oversight easier, it does come with its share of challenges including regulator hesitancy which stems from historical concerns regarding bias, discrimination, and privacy, which has now ultimately led to some calling for policymakers to introduce overly burdensome regulations on the technology. Nonetheless, the adoption of artificial intelligence will continue to grow, as its already demonstrated immense value playing a significant role in the future of banking and finance.

Concerns over AI and the Potential to Regulate

Artificial intelligence has proved and continues to prove itself to be one of the most valued assets, across all industries, that enables companies to rapidly evolve and conduct their business more effectively and efficiently. However, it does bring with it criticism and concern in which regulators and skeptics are asking the question, can artificial intelligence be transparent? Can we trust it? There have been reports of bias, discrimination, invasiveness to private data and violations of human rights, which has led the European Union to propose the Artificial Intelligence Act which will aim to impose restrictions on artificial intelligence in an effort to regulate the technology and eliminate these instances. While the law would impact all industries, there would be a particular focus on high-impact sectors which includes both the banking and finance industries.

While these regulations seem to have the publics best interest and values in mind, it also proposes an impractical solution, especially if other countries seek to implement them. According to the Center for Data Innovation, these new regulations would cost the European economy more than $30 million to introduce and manage. Should policymakers enact these regulations, they would ultimately spend more on regulating the technology than the cost of compliance itself, making it illogical. The banking and finance industries have already seen artificial intelligence at work and have reaped the benefits and any service less than what theyre accustomed to would be a setback.

One of the major concerns from a technology standpoint is that artificial intelligence relies on what is referred to as the black box, which means regulators are able to see what goes into the AI and what comes out but are unable to see how the algorithms and technology actually works. Tech companies are hesitant to share their algorithms whats inside the black box because it can lead to potential intellectual property infringement and theft. While the European Union continues to push for some type of reform, it is important they realize that they need to work directly with tech companies to implement risk management solutions that provide thorough training and regular testing to offer protections against biases.

Communication in Todays Digital and Remote Work Environment

Over the last few decades, there has been a steady stream of new communication methods that have surfaced including SMS, Slack, WhatsApp, etc. As these new channels have become dominant in the way we communicate daily in both our personal and professional lives, it has become harder and nearly impossible for compliance teams to provide security oversight and carry out thorough reviews and investigations. In 2020, financial organizations saw an increase of 54% in tickets coming in through WhatsApp. While new communication channels are making it easier and are being more heavily relied on to communicate, its also indirectly creating opportunities for security and compliance breaches.

These new channels alone call for the use of artificial intelligence to make compliance more effective and efficient. However, with the new workplace environment where many are working outside of the office and from home, it is even more critical for banking and financial institutions to find a better way to maintain compliance regulations. With a dire need to address compliance across all communication channels, many are turning to artificial intelligence as a solution as it provides automated compliance and security oversight that is more advanced and tech-driven especially when communicating over encrypted messaging services. This ultimately will provide banks with a valuable resource that allows employees to continue using messaging apps and services that are in line with privacy laws like GDPR, ensuring separation between personal and business data.

AIs Value to Compliance in the Banking and Finance Industries

Financial institutions have struggled with communication storage and while still new to the industry, artificial intelligence provides the support and solution to overcome. Artificial intelligence enables compliance officers and institutions the ability to automate all elements of their communications data management. This includes capturing data, enriching it with third party data such as CRM, normalizing it and allowing the compliance officer the ability to seamlessly investigate, archive and retain data. Under regulatory rules, there is an immense amount of both structured and unstructured data which is required for recordkeeping. Additionally, artificial intelligence can combine all data sources which in turn makes advanced searches and the ability to perform full investigations more efficiently.

In addition to the previously mentioned capabilities, artificial intelligence is revolutionizing financial compliance and redefining the way in which communications compliance risk is managed. Financial institutions are able to mitigate risk, improve operational efficiency, and reduce compliance costs by being afforded the ability to proactively detect and alert on abnormalities in communication including threats or violations such as insider trading, providing an in-depth analysis and breakdown of various communication triggers, and offering the ability to manage and customize alerts based on their specific needs, regulations, and procedures.

Implementing Artificial Intelligence Responsibly

For any organizations or institutions that decide to implement artificial intelligence for compliance purposes, it is incredibly important that it is done so responsibly. It is critical that banking and finance organizations work with companies who can fulfill their internal needs as well as meet industry standards and regulations. With much discussion around transparency, it is key to ensure the ability to separate personal data from professional data, conduct business without the consequences of bias and discrimination, and offer data that is easily interpreted and explained by compliance team members. These capabilities are readily available and continue to be top of mind when creating and developing new artificial intelligence models and algorithms.

Compliance and security measures are in place to keep our businesses and employees safe and protected from various risks and market abuse. The fact of the matter is that the old way of doing things just cant keep up and is no longer an effective process.

While trust from regulators, or lack thereof, continues to play a role in the acceptance of artificial intelligence, it will not prevent companies from adoption and implementation as long as new opportunities continue to arise to mitigate risk, cut compliance costs, and increase operational efficiency. Artificial intelligence is only going to continue to develop and evolve, leaving banking and financial institutions to get on board to keep up with the fast pace of communication and do their due diligence in following regulations. The alternative? Continue to rely on outdated legacy vendors and risk failing to maintain industry wide regulations. Artificial intelligence can and will drastically improve compliance and security oversight.

See the original post:

The Role of Artificial Intelligence in Compliance and Security Oversight - International Banker

Automation will continue to grow and expand within logistics operations through the use of technologies such as artificial intelligence.

Automation uses technology to augment human effort across a myriad of tasks. In logistics, the potential for automation is massive, and the benefits are significant, especially when operations experience large variations or increases in demand. Scaling operations up typically requires additional staff who are often not immediately available, particularly during times when demand is also coming from other industries. Reacting quickly to market fluctuations requires fast action and additional capacity across the entire operation.

Logistics automation allows for rapid increases in capacity as demand changes. When used strategically, logistics automation increases productivity, reduces human error, and improves working efficiency. And with the right logistics automation software, hardware, and platforms resources in place, the impact on operational expenditures during periods of low demand are minimal and much lower than maintaining a large human workforce. As demand increases, the capacity is already in place and ready to be activated. While this gives logistics companies the flexibility needed to react quickly to changes in demand, there is the opportunity to do more.

See also: Logistics Market Needs Digital Transformation to Overcome Challenges

The introduction of artificial intelligence (AI) into logistics automation amplifies AIs impact. AI reduces errors in common semi-skilled tasks such as sorting and categorizing products. Autonomous mobile robots (AMRs), for instance, improve package delivery, including the last mile of delivery which is typically the most expensive. AI helps AMRs with route planning and feature recognition, such as people, obstacles, delivery portals, and doorways.

Integrating logistics automation into any environment comes with challenges. It can be as simple as replacing a repetitive process with a powered conveyor or as complex as introducing a collaborative, autonomous robot into the workplace. When AI is added to this automation and integration process, the challenges become more complex, but the benefits also increase.

The effectiveness of individual automation elements increases as the solutions become more connected and more aware of all the other stages in the process. Putting AI closer to where the data is generated, and actions are taken, is referred to as edge AI. The adoption of edge AI is already redefining logistics automation.

Edge AI is developing rapidly, and its use is not restricted to logistics automation. The benefits of putting AI at the network edge have to be balanced with the availability of resources, such as power, the environmental operating conditions, the physical location, and the space available.

Edge computing brings computation and data closer together. In a traditional IoT application, most data is sent over a network to a (cloud) server, where the data is processed, and results are sent back to the edge of the network, such as at the physical piece of equipment. Cloud-only computing introduces latency, which is unacceptable in time-critical systems. One example where edge computing comes into play is capturing and processing the image data of a package locally during sorting enables the logistics automation system responds in as little as 0.2 seconds. Network latency in this part of the system would slow down the sorting process, but edge computing is removing that potential bottleneck.

While edge computing brings the computation closer to the data, adding AI to the edge makes the process more flexible and even less prone to error. Similarly, last-mile logistics relies heavily on humans, but this too is improved with AMRs using edge AI.

Adding AI has a significant impact on the hardware and software used in logistics automation, and there is an increasing number of potential solutions. Typically, the solutions used to train an AI model are not suitable for deploying the model at the networks edge. The processing resources used for training are designed for servers, where resources such as power and memory are almost infinite. At the edge, power and memory are far from infinite.

In terms of hardware, large multicore processors are not well suited for edge AI applications. Instead, developers are turning to heterogeneous hardware solutions optimized for AI deployment at the edge. This includes CPUs and GPUs, of course, but it extends to application-specific integrated circuits (ASICs), microcontrollers (MCUs), and FPGAs. Some architectures, like GPUs, are good at parallel processing, while others, like CPUs, are better at sequential processing. Today, there is no single architecture that can really claim to provide the best solution for an AI application. The general trend is to configure systems using the hardware that offers the most optimal solution, rather than using multiple instances of the same architecture.

This trend points towards a heterogeneous architecture, where there are many different hardware processing solutions configured to work together, rather than a homogeneous architecture that uses multiple devices all based on the same processor. Being able to bring in the right solution for any given task, or consolidate multiple tasks on a specific device, provides greater scalability and the opportunity to optimize for performance per watt and/or per dollar.

Moving from a homogeneous system architecture to heterogeneous processing requires a large ecosystem of solutions and a proven capability to configure those solutions at the hardware and software level. Thats why its important to work with a vendor that has significant tier 1 partnerships with all the major silicon vendors, offering solutions for edge computing and working with them to develop systems that are scalable and flexible.

In addition, these solutions use general open-source technologies like Linux, as well as specialist technologies such as the robot operating system, ROS 2. In fact, there is a growing number of open-source resources being developed to support both logistics and edge AI. There is no single right software solution from this point of view, and the same is also true for the hardware platform on which the software runs.

To increase flexibility and reduce vendor lock-in, one approach is to use modularization at the hardware level, making hardware configuration within any solution more flexible. In practice, modularization at the hardware level allows an engineer to change any part of the systems hardware, such as a processor, without causing system-wide disruptions.

The ability to upgrade an underlying platform (whether that be software, processors, etc.) is particularly important when deploying a new technology like edge AI. Every new generation of processor and module technology often provides a better power/performance balance for an inferencing engine operating at the networks edge, so being able to take advantage of these performance and power gains quickly and with minimal disruption to the overall logistics automation system and edge AI hardware system design is a distinct advantage.

Modularization in the hardware is extended into the software by using a micro-service architecture and container technology like Docker. If a more optimal processor solution becomes available, even if it is from a different manufacturer, the software leveraging the processor is modularized and can be used in place of the module for the previous processor without changing the rest of the system. Software containers also provide a simple and robust way to add new features, which applies to running AI at the edge, for example.

The software inside of a container can also be modularized.

A modular and container approach to hardware and software minimizes vendor lock-in, meaning a solution is not tied to any one particular platform. It also increases the abstraction between platform and application, making it easier for end-users to develop their own applications that are not platform-dependent.

Deploying edge AI within logistics automation doesnt require replacing entire systems. Start by assessing the workspace and identifying stages that can really benefit from AI-powered automation. The main objective is to increase efficiency while decreasing operation expenditure, particularly in response to increased demand during a time of labor shortages.

There is an increasing number of technology companies working on AI solutions, but often these are aimed at the cloud, not edge computing. At the edge, the conditions are very different, resources may be limited, and there may even be a need for a dedicated private communications network.

Automation will continue to grow and expand within logistics operations through the use of technologies such as AI. These system solutions need to be designed for use in harsh environments, very different from the cloud or data center. We address this using a modular approach that offers highly competitive solutions, short development cycles, and flexible platforms.

Visit link:

Artificial intelligence is Changing Logistics Automation - RTInsights