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UPS Committed to Electric Vehicles on an AI Platform


UPS has made a major commitment to electric delivery vehicles incorporating AI, including these bikes in Paris. (UPS PRESS SERVICE)

By AI Trends Staff

UPS, the logistics and delivery company, is spearheading a project in England to explore how AI systems can optimize the charging of electric fleet vehicles, and help integrate onsite renewable energy resources at vehicle depots.

The EV Fleet-Centered Local Energy Systems (EFLES) project is scheduled to start in May at the UPS depot in the Camden borough of London, according to a recent account in electrive. The UK Power Networks Services will provide oversight, while the smart battery and EV-charging software provider Moixa will contribute its GridShare smart AI platform to manage solar, storage and charging assets.

“We have the global expertise, smart-charging infrastructure and resources to host this first-of-a-kind test bed at our Camden facility,” stated UPS sustainable development coordinator Claire Thompson-Sage. “This project will build on our EV infrastructure technology to help develop a holistic local energy system.”

Claire Thompson-Sage, UPS Sustainable Development Coordinator

The GridShare software helps track hundreds of data sources for energy prices, power demand, weather conditions and more, to help determine which charging times are less expensive and which mix of renewable energy makes the most sense at any given point in time.

The ERLES project is the next stage in the UPS partnership with Arrival, the UK-based “generation 2” electric vehicle manufacturer, which developed its newest vehicle with UPS.

UPS recently placed an order for 10,000 electric vehicles from Arrival, to be delivered from 2020 to 2024, according to a recent press release issued by Arrival.

UPS co-developed the Generation 2 vehicles with Arrival, which employed a new method of assembly using low capital, low-footprint micro-factories located to serve local communities and be profitable making thousands of units. The UPS partnership with Arrival was first announced in 2016.

“UPS has been a strong strategic partner of Arrival, providing valuable insight to how electric delivery vans are used on the road and how they can be optimized for drivers, stated Denis Sverdlov, founder and CEO of Arrival. “Together our teams have been creating bespoke [custom] electric vehicles, based on our flexible skateboard platforms, that meet the end-to-end needs of UPS from driving, loading/unloading, depot and back office operations.”

Denis Sverdlov, founder and CEO of Arrival

Carlton Rose, President of UPS Global Fleet Maintenance & Engineering, stated, “Our investment and partnership with Arrival is directly aligned with UPS’s transformation strategy, led by the deployment of cutting-edge technologies.These vehicles will be among the world’s most advanced package delivery vehicles, redefining industry standards for electric, connected and intelligent vehicle solutions.”

UPS Has Had Long Commitment to Electric Vehicles

UPS had 1,000 electric vehicles in its fleet of 112,000 vehicles two years ago. The cost of the vehicle new was found to be no more than regular diesel vehicles, because the cost of electric batteries plummeted 80 percent in six years, according to a UPS press release from April 2018.The electric transporters are expected to create additional value of UPS in operational savings and routing efficiency.

In the U.S., UPS has been working with Workhorse to develop an electric transport vehicle. The target at the outset was a range of 100 miles, with similar procurement costs as an internal combustion motor. The founder and CEO of Workhorse, Steve Burns, late last year bought the Lordstown, Ohio electric vehicle plant from General Motors, through a company he set up to execute the transaction, Lordstown Motors. He has said he wants to build electric pickup trucks for “business and government customers” and has decided the name of the first model will be: Endurance.

Financially, Workhorse has faced some challenges, losing $38 million in 2019 and having little sales in late 2019, according to a recent account in The Verge. Workhorse will own 10 percent of Lordstown Motors, and license to it the intellectual property related to the planned W-15 electric pickup truck. Burns will transfer 6,000 pre-orders for the truck to Lordstown. He is searching for financing, saying he needs $300 million to start product in a year. He plans to run a union shop and produce 500,000 vehicles per year, double the number of Cruze sedans GM made at the plant.

In the case of these new trucks, UPS worked closely with a supplier, Workhorse, to redesign the trucks “from the ground up,” stated Scott Phillippi, UPS’s senior director of maintenance and engineering. Phillippi expects the new design will reduce the truck’s weight by some 1,000 pounds, compared with a diesel or gas-powered vehicle. That plus better batteries will give the truck an electric range of around 100 miles, enough for most routes in and around cities.

Read the source articles and releases at electrive, a UPS press release on the 10,000 vehicle order from Arrival,  and in The Verge., and a UPS press release from April 2018.

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AI Helping Customer Analytics Dive Deeper into Customer Experience

Sephora is credited with making good use of AI in customer analytics with its Visual Artist product, that lets visitors try on cosmetic products. The company has “considered the entire customer journey,” suggests one observer. (GETTY IMAGES)
By AI Trends Staff
Corporate marketers are using AI to more deeply analyze …

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Does Your Business Really Need an AI Solution?

There is an incredible amount of hype about AI today. Businesses across various industries continue to adopt this technology to receive a competitive advantage over others, reduce operating costs, and improve customer experience. But does your business really need an AI solution?
Some businesses have said that Artificial Intelligence is …

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AI Spawning New Products in Investment Business


A financial data analysis graph. Selective focus. Horizontal composition with copy space.

By AI Trends Staff

AI is helping to launch new products in the investment business, from new data services to new theories of investing.

For example, Liquidnet recently announced plans to launch a data service for money management firms that uses AI to search for hidden information that can affect investment decisions, according to an account in WSJPro. Liquidnet operates a dark pool, a private financial forum for buying and selling securities, that lets investors stay hidden until a trade is executed.

The new service, called Investment Analytics, will use AI to analyze financial reports, earnings calls, news articles, and other sources. The company appointed Vicky Sanders, a co-founder of RSRCHXchange, which Liquidnet acquired last year, as the global head of Investment Analytics. RSRCHXchange was a financial tech company that provided asset management firms with a repository of reports based in the cloud.

Vicky Sanders, Global Head of Investment Analytics

The plan is to combine the services with two other recent Liquidnet acquisitions, OTAS Technologies and Prattle, to use AI for a new investment product. OTAS uses AI to analyze data on liquidity, volumes, and spreads. Prattle uses natural language processing and machine learning to analyze publicly-available content.

“The strength really comes if you can put this technology together and really start leveraging all the data in a combined offering,” stated Larry Tabb, founder and research chairman of capital-markets research and consulting firm Tabb Group. “That’s what Liquidnet is betting—that the AI will be able to do it better, faster, more efficiently and more effectively than analysts,” he stated.

The new service is in a pilot phase; Sanders said the way portfolio managers gather information has many inefficiencies that AI can help to streamline.

Demographic Over 60 Attracting New Attention

People over 60 number about one billion and have historically been ignored by the investment industry. The global spending power of the demographic, estimated at $15 trillion this year, according to an account in Forbes, is attracting attention. The industry is looking at Longevity Banks and FinTech 2.0 services to help the segment optimize their wealth.

“In the next few years, age-friendly FinTech companies and Longevity Banks will develop new financial products designed for clients who are planning to live extra long lives and want to remain high functioning and financially stable throughout.” stated author Margaretta Colangelo, Co-founder and Managing Partner of Deep Knowledge Group, a consortium of commercial and non-profit organizations active on many fronts.

Margaretta Colangelo, Co-founder and Managing Partner of Deep Knowledge Group

Increasing life expectancy is helping to articulate a market called Longevity Banks, which have more time to accumulate wealth and have a longer investment horizon. Appealing to this group will be “new products that provide a comprehensive view of investments, taxes, insurance, and regulation without unneeded complexity.”

Research is going on into the ideas. The Longevity AI Consortium at King’s College London is developing methods for translating advanced AI for longevity products, including new applications of life data for insurance companies, pension funds, healthcare companies and government bodies. The Consortium this year plans to expand to Switzerland, Israel, Singapore, and the US. The work is expected to lead to the combination of innovative AI and wealth management, resulting in the creation of new financial institutions optimized for an aging population.

Bank of America’s Erica Has 10 Million Users

Meanwhile, back in the present, the banking industry talks a good game about using AI, but they are not as proficient at using the technology as public announcements would suggest. While some investment in advanced analytics is happening, most banks are continuing to focus on back-office efficiency, risk avoidance and cost reductions, according to a recent account in The Financial Brand.

One exception to the internal focus of AI implementation is at the Bank of America, where the AI-powered digital assistant, Erica, has more than 10 million users and completed 100 million client requests in the 18 months since its introduction. According to Bank of America,  “The app can be configured to a person’s preferences and usage, giving everyone a different home page — similar to the way Amazon and Netflix give every user a different home screen.”

Few banks can match this capability of being able to notify a customer of a potential overdraft, remind the customer of a recurring payment or understand a customer’s spending habits.

Read the source articles in WSJPro,  Forbes and  The Financial Brand.

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Insurance Companies Using AI to Build Safety Systems, Optimize Rates 


Leading companies in the $500 billion/year auto insurance business are employing AI to gain competitive advantage, while startups are using AI to gain a foothold. Credit: Getty Images 

By AI Trends Staff 

Leading insurance companies in the $500 billion/year insurance industry are studying what types of ML applications to try to gain a business advantage, and startups are using AI to disrupt the industry.  

Safety is a big focus, timely considering that motor-vehicle fatalities in 2016 peaked at 40,200; the highest amount recorded in nearly a decade. The estimated healthcare costs to people injured in car crashes totaled over $80 billion. Insurance adjusters who assess auto damage earned an average salary over $63,000 in 2016, according to the Bureau of Labor Statistics.  

A look at AI initiatives at four leading insurance companies was recently published in emerj. 

State Farm launched an online competition in 2016 to help develop a system using computer vision to identify distracted drivers. Some 1,440 participated; the company offered $650,000 total in three prize levels. State Farm provided a dataset of photos of drivers from dashboard cameras. The challenge was to classify the perceived behavior of each driver user categories including texting, talking on the phone and operating the radio. 

Scores were compiled using a metric of a minimum value of zero to a maximum value of one. The goal is to achieve a score as close to zero as possible, indicating higher accuracy. The winning application achieved a score of .08739 using two neural network models and image classification based on regions of the image, including the bottom right quarter where the driver’s hand is usually visible.  

State Farm has launched a Drive Safe & Save program that provides discounts of up to 30% to motorists who enroll.  

Liberty Mutual in 2017 announced plans to develop apps with AI capability and products aimed at improving driver safety. The company established an innovation incubator, Solaria Labs, to develop an open API developer portal to help the effort along. The company is believed to be working on an app to help drivers involved in a car accident quickly assess the damage to their car using the smartphone camera. A database of thousands of car crash images will be referenced to generate a repair estimate. 

Liberty Mutual has also launched a $150 million venture capital initiative, Liberty Mutual Strategic Ventures (LMSV), to focus on innovative technology and services for the insurance industry. Among the companies receiving investment is Snapsheet, a smartphone application that enables users to receive auto repair bids from local body shops within 24 hours. The company uses AI and machine learning to support its data analysis.  

Many startups see an opportunity to disrupt the auto insurance business; a number were mentioned in a recent account in builtin.  

Insurify, for example, was founded in 2013 as a spinoff of the MIT $100k Pitch competition; it’s official site launched in 2016. The site enables insurance shoppers to take coverage needs into their own hands. Founder and CEO Snejina Zacharia has led the company to over $25 million in funding and secured $10 billion in insurance coverage to date. The company has achieved a 4.8 out of 5.0 possible rating on ShopperApproved, averaging responses from 2,500 reviews.  

INSHUR is aimed at helping rideshare drivers using Uber or Lyft, and limousine drivers, to find competitive rates for auto insurance. Founded in 2016, the company is based in New York City, is backed by Munich Re Digital Partners, and launched in the UK in 2018. INSHUR has signed up over 40,000 drivers. The company supports liability and physical damage policies with minimum limits of insurance as required by the NYC Taxi & Limousine Commission (TLC) for limousines, which is also compatible with requirements for ride sharing services. 

Nauto aims to help commercial fleets avoid collisions using the AI-powered Driver Behavior Learning Platform to reduce driver distraction and prevent collisions. The system includes dual-facing cameras, computer vision and proprietary algorithms to assess how drivers are interacting with vehicles and the road, to pinpoint and prevent risky behavior in real time. Nauto has analyzed billions of data points from over 400 million video miles analyzed with AI; its machine learning algorithms are continuously improving. Commercial fleets using Nauto have avoided an estimated 25,000 collisions, resulting in an estimated $400 million in savings. 

Read the source articles in emerj and builtin. 

Source: AI Trends

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State of the Market: What AI Implementations Are in Place and Underway


IT Automation was the leading AI project to be implemented among readers of AI Trends, chosen by 39% of readers surveyed. (GETTY IMAGES)

In a broad market survey conducted recently by AI Trends, respondents outlined which AI solutions they have implemented and which they expect to implement in the coming months. Together, the results reveal which areas are ripe for the most growth in short-term AI implementation.

AI Trends surveyed readers representing 28 different industries, though IT services, computer software, and government made up well over a third of the responses. We asked which AI solutions have been implemented so far, and which are coming in less than 12 months.

Among the solutions already implemented, IT automation leads the pack with 39% of responses, though cybersecurity, customer services, and virtual assistants each accounted for more than 25% of the responses. Sales optimization and workforce management accounted for the fewest votes at only 13% each.

In the coming 12 months, IT automation is still the leader, though forecasting is a close second with 27% of respondents mentioning that as an area of emphasis. Workforce management remains a fairly low priority with only 14% of respondents choosing that option. Sales optimization, though, was included as a priority for 19% of survey-takers.

Nearly one-third of respondents said implementing AI has given the company a slight lead over competitors, while just slightly fewer said the technology has “allowed us to remain competitive.”

For the “why” within the business, the most respondents chose enhancing customer experience and enhancing existing products as reasons to implement AI. Optimizing internal operations including reducing headcount through automation and streamlining employee roles were chosen less frequently by respondents, but all three options earned at least 17% of answers.

With implementations underway and new ones on the horizon, AI skills continue to be a challenge. Data scientists (24%) and AI software developers (23%) were the top skill sets that companies found lacking in house when they began to implement AI solutions. But AI researchers, project managers, and user experience designers are also needed.

See the full survey results.

Source: AI Trends

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US Patent and Trademark Office Seeking Comment on Impact of AI on Creative Works


A notice in the Federal Register invited readers to comment on whether creative content produced by AI can be issued a patent.

By AI Trends Staff

The US Patent and Trademark Office (USPTO) is getting more involved in AI. One effort is an AI project that aims to speed patent examinations. The office receives approximately 2,500 patent applications per day.

The project took some nine months to develop and makes a “really compelling case” for the use of AI, stated Tom Beach, Chief Data Strategist and Portfolio Manager at USPTO, in an account in MeriTalk. Beach was speaking at a recent Veritas Public Sector Vision Day event.

The project calls for extracting technical data from patent applications and using that to enhance Cooperative Patent Classification (CPC) data, which is reviewed by USPTO patent examiners to evaluate patent applications. The aim is to speed the overall evaluation process. “That’s the ROI for this project,” Beach stated.

The USPTO is also actively seeking comments on the impact of AI on creative works. The office published a notice in the Federal Register in August 2019 seeking comments. It sought comment on the interplay between patent law and AI. In October, the USPTO expanded the inquiry to include copyright, trademark and other IP rights, according to an account in Patently-O. Comments are now being accepted until Jan. 10, 2020.

(Anyone can respond; interested AI Trends readers are encouraged to respond.)

The questions have no concrete answers in US law, experts suggest. “I think what’s protectable is conscious steps made by a person to be involved in authorship,” stated Zvi S. Rosen, lecturer at the George Washington University School of Law, in an account in The Verge. A person executing a single click might not be so recognized. “My opinion is if it’s really a push button thing, and you get a result, I don’t think there’s any copyright in that,” Rosen stated.

This push-button creativity discussion gets a little more murky when considering the deal Warner Music reached with AI startup Endel in March 2019. Endel used its algorithm to create 600 short tracks on 20 albums that were then put on streaming services, returning a 50 / 50 royalty split to Endel, The Verge reported.

Rosen encouraged people to respond. “If a musician has worked with AI and can attest to a particular experience or grievance, that’s helpful,” he stated.

For those interested, here are the questions:

  1. Should a work produced by an AI algorithm or process, without the involvement of a natural person contributing expression to the resulting work, qualify as a work of authorship protectable under U.S. copyright law? Why or why not?
  2. Assuming involvement by a natural person is or should be required, what kind of involvement would or should be sufficient so that the work qualifies for copyright protection? For example, should it be sufficient if a person (i) designed the AI algorithm or process that created the work; (ii) contributed to the design of the algorithm or process; (iii) chose data used by the algorithm for training or otherwise; (iv) caused the AI algorithm or process to be used to yield the work; or (v) engaged in some specific combination of the foregoing activities? Are there other contributions a person could make in a potentially copyrightable AI-generated work in order to be considered an “author”?
  3. To the extent an AI algorithm or process learns its function(s) by ingesting large volumes of copyrighted material, does the existing statutory language (e.g., the fair use doctrine) and related case law adequately address the legality of making such use? Should authors be recognized for this type of use of their works? If so, how?
  4. Are current laws for assigning liability for copyright infringement adequate to address a situation in which an AI process creates a work that infringes a copyrighted work?
  5. Should an entity or entities other than a natural person, or company to which a natural person assigns a copyrighted work, be able to own the copyright on the AI work? For example: Should a company who trains the artificial intelligence process that creates the work be able to be an owner?
  6. Are there other copyright issues that need to be addressed to promote the goals of copyright law in connection with the use of AI?
  7. Would the use of AI in trademark searching impact the registrability of trademarks? If so, how?
  8. How, if at all, does AI impact trademark law? Is the existing statutory language in the Lanham Act adequate to address the use of AI in the marketplace?
  9. How, if at all, does AI impact the need to protect databases and data sets? Are existing laws adequate to protect such data?
  10. How, if at all, does AI impact trade secret law? Is the Defend Trade Secrets Act (DTSA), 18 U.S.C. 1836 et seq., adequate to address the use of AI in the marketplace?
  11. Do any laws, policies, or practices need to change in order to ensure an appropriate balance between maintaining trade secrets on the one hand and obtaining patents, copyrights, or other forms of intellectual property protection related to AI on the other?
  12. Are there any other AI-related issues pertinent to intellectual property rights (other than those related to patent rights) that the USPTO should examine?
  13. Are there any relevant policies or practices from intellectual property agencies or legal systems in other countries that may help inform USPTO’s policies and practices regarding intellectual property rights (other than those related to patent rights)?

Read the source articles in  MeriTalk, Patently-O and The Verge.

Send your comments to AIPartnership@uspto.gov.

Source: AI Trends

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Using AI In Content Is An Enterprise Imperative


Content intelligence can employ computer vision, machine learning, natural language processing and content analytics to make unstructured assets available to the digitized system. (GETTY IMAGES)

Contributed Commentary by Ivan Yamshchikov, ABBYY

Ivan Yamshchikov, AI Evangelist, ABBYY

The goal to be a data-driven organization has been a rallying cry for enterprises for the past decade. The aspiration is to leverage content to gain powerful insights into ways businesses can better serve customers, improve operational efficiency and respond to market dynamics faster. However, these efforts have come up short and have not been attainable at the promised level—until now.

Advances in AI technology are enabling organizations to maximize the value of their content.

Getting Content AI Ready

Most future-focused enterprises have already solved the problem of data to a large extent. They know how to digitize it with advanced OCR technology and store it within CRM, BPM, and ERP systems, which is the first step for readying content for AI. They also have successful use cases of applying machine learning to make the processing into systems more efficient, such as in the processing of invoices.

Formtran, for example, is a systems integrator that digitizes and processes more than 50 million pages per year of various types of unstructured documents, such as invoices, real estate documents, and check and wire transfer documents. They do this for global organizations in consumer goods, financial services and ecommerce, and illustrate how businesses are digitizing and transforming unstructured data in order to maximize the value of content and information assets.

After digitization, data is ready to be infused with a set of technologies known as content intelligence (or content IQ) that use AI to carry out tasks such as reading and categorizing a document, routing a document, extracting and validating data from documents, and other tasks related to understanding and processing unstructured content. Content intelligence is flourishing with companies expecting to increase spend on these technology over the next year by 31%.

By incorporating content IQ, organizations are able to make quicker, more accurate decisions and deliver greater business value. These technologies include computer vision, machine learning, natural language processing and content analytics.

Factors Driving Content Intelligence

Within the past few years, the biggest demand for AI was in the automation of repetitive tasks via robotic process automation (RPA) software robots, or digital workers. However, organizations are now demanding greater insights from their complex and semi-structured data-streams other than simplistic automation.

To understand the need and drivers for content IQ, leading research firm IDC conducted a global survey among 500 IT executives. Respondents indicated manual sorting and classification of documents, manual data extraction from documents, inadequate compliance with security/privacy regulations, and poor data, errors, and inaccuracy of information as top pain points that content IQ can address (see chart).

These pain points highlight that poorly managed, unstructured content leads to lack of customer information, which in turn leads to inadequate data to support decision making. Furthermore, lack of control over content can leave sensitive data vulnerable to risk and fraud.

Business Benefits of Content Intelligence

The IDC survey also found there are a number of benefits by deploying content IQ, ranging from increased employee productivity to increased customer satisfaction. Over one-third of respondents saw an improvement in responsiveness to customers, new product or revenue opportunities, increased visibility and/or accountability, or increased customer engagement.

For example, solution integrator Ripcord is leveraging content IQ technology for Coca-Cola Bottlers’ Sales and Services (CCBSS). Ripcord is digitizing millions of proof of delivery documents for CCBSS’s key fulfillment and logistics records using RPA and intelligent capture. Its chief procurement officer says the company will increase operational efficiencies, customer service, and add innovative value and contribute to financial risk mitigation for their bottling partners by using these technologies.

Another notable business benefit is it can be used to augment human labor. Content IQ fosters a new era of human-machine collaboration where machines will increasingly perform cognitive skills such as evaluating information, reasoning, and decision-making and administering (see chart). In fact, IDC estimates digital workers will increase by 50% within the next two years.

The increase in digital workers enables organizations to redirect human workers to higher-value tasks. With invoices, for example, machines can transfer data from invoices into ERP systems and authorize it for payment when all conditions are met. Staff are then able to investigate and expedite any exceptions to ensure delays are minimized. The great thing about content IQ is its machine learning capabilities learn how to handle exceptions over time. There is a clear adoption trajectory​ as the system evolves and learns. Machine augmentation becomes the source of additional data and further improvement for the AI algorithms that enable the transfer from an augmented process to an automated process.

Need for CEO Support

For content intelligence and any AI initiative to be successful, there must be a strong vision and support from the C-suite. According to IDC, a lack of upper management support along with siloed business units and disparate legacy systems were all barriers to executing content IQ.

The good news is that most organizations are realizing the importance of senior management buy-in and that content intelligence is imperative to make their digital transformation successful. Seventy-eight percent of IDC’s respondents said the CEO was the top decision maker for content IQ, and half also included the CIO, followed by the CTO and chief digital officer as decision makers along with line of business executives. Furthermore, there is a growing trend among organizations to create a center of excellence (COE) which embodies key stakeholders for identifying and deploying AI initiatives.

Content IQ opportunities are omnipresent across all industries, with the most spending taking place among industries heavy laden with content such as financial services, transportation and logistics, insurance, and healthcare. It is also beneficial in horizontal business processes such as invoice processing, customer onboarding, claims processing and wherever you would connect customer-facing applications, such as mobile apps and chat bots, with backend systems. A common scenario is an organization will incorporate content IQ in a finance function and the project lead, amazed by the results, evangelizes the benefits with other business functions. They are able to transfer results and methods from one application to another in a straightforward manner making deployment faster.

Successful organizations will understand the need for AI in content and embrace the opportunities within their business processes. IDC provides three tips to ensure your content IQ investment goes smooth: invest in modular, enterprise wide digital platforms; develop a strategy for ongoing training and development of employees to ensure they have the digital skills set; and think human-machine collaboration as the new normal.

Ivan Yamschchikov is AI Evangelist at ABBYY. He currently works as a Post-Doc Researcher at the Max Planck Institute for Mathematics in Germany.  Ivan received a PhD from Technical University Cottbus-Senftberg, Germany and MSc in Mathematical Physics from Saint-Petersburg State University, Russia. He is fluent in English, Russian and German with good knowledge of Bulgarian, French, Japanese and Swedish. He can be reached at ivan.yamshchikov@abbyy.com.

Source: AI Trends