Daily Archives: December 13, 2019

The "Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024" report has been added to ResearchAndMarkets.com's offering.

The global AI in the drug discovery market is projected to reach USD 1,434 million by 2024 from USD 259 million in 2019, at a CAGR of 40.8% during the forecast period.

Growing number of cross-industry collaborations and partnerships and the need to control drug discovery & development costs and reduce the overall time taken in this process are the key factors driving the AI in the drug discovery market.

Growth in this market is mainly driven by growing number of cross-industry collaborations and partnerships, the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications & services, and the impending patent expiry of blockbuster drugs. On the other hand, a lack of data sets in the field of drug discovery and the inadequate availability of skilled labor are some of the factors challenging the growth of the market.

The immuno-oncology segment accounted for the largest share in 2019.

Based on application, the artificial intelligence in the drug discovery market is segmented into neurodegenerative diseases, immuno-oncology, cardiovascular disease, metabolic diseases, and other applications. The immuno-oncology segment accounted for the largest share of 44.6% of the AI in the drug discovery market in 2018, owing to the increasing demand for effective cancer drugs. Neurodegenerative diseases form the fastest-growing application segment, with a CAGR of 42.9% during the forecast period. The ability of AI to discover drugs for complex diseases and the emphasis of market players on providing AI-based platforms for neurological diseases are responsible for the fast growth of this application segment.

The Research centers and academic & government institutes segment to register the highest growth rate in the forecast period.

Based on end-user, the AI in the drug discovery market is segmented into pharmaceutical & biotechnology companies, contract research organizations, and research centers and academic, & government institutes. In 2018, the pharmaceutical & biotechnology companies segment accounted for the largest share in the AI in the drug discovery market. AI and machine learning to allow pharmaceutical companies to operate more efficiently and substantially improve success rates at the early stages of drug development. This is one of the major factors driving the growth of this market. Research centers and academic & government institutes are expected to show the highest growth of 43.6%.during the forecast period. The growth of the CROs segment is tied to that of pharmaceutical & biotechnology companies, as the rise in research and production activity will ensure sustained demand for contract services.

North America to be the largest and the fastest-growing regional market.

North America, which comprises the US, Canada, and Mexico, forms the largest market for AI in drug discovery. These countries have been early adopters of AI technology in drug discovery and development. In the North American market, the US is a significant contributor. Also, prominent AI technology providers, such as IBM, Google, Microsoft, NVIDIA, and Intel, are headquartered in the US; their strong presence is a key contributor to market growth. Other drivers include the well-established pharmaceutical industry, high focus on R&D & substantial investment, and the presence of globally leading pharmaceutical companies. These are some of the major factors responsible for the large share and high growth rate of this market.

Key Topics Covered:

1 Introduction

1.1 Objectives of the Study

1.2 Market Definition

1.3 Market Scope

1.3.1 Markets Covered

1.3.2 Years Considered for the Study

1.4 Currency

1.5 Limitations

1.6 Stakeholders

2 Research Methodology

2.1 Research Data

2.1.1 Secondary Sources

2.1.2 Primary Sources

2.2 Market Size Estimation

2.3 Market Breakdown and Data Triangulation

2.4 Assumptions for the Study

Story continues

3 Executive Summary

4 Premium Insights

4.1 Market Overview

4.2 Market, By Offering (2019-2024)

4.3 Market for Machine Learning, By Type & Region (2018)

4.4 Market: Geographic Growth Opportunities

5 Market Overview

5.1 Introduction

5.2 Market Dynamics

5.2.1 Market Drivers

5.2.2 Market Opportunities

5.2.3 Market Challenges

6 Market, By Offering

6.1 Introduction

6.2 Software

6.3 Services

7 Market, By Technology

7.1 Introduction

7.2 Machine Learning

7.3 Other Technologies

8 Market, By Application

8.1 Introduction

8.2 Immuno-Oncology

8.3 Neurodegenerative Diseases

8.4 Cardiovascular Disease

8.5 Metabolic Diseases

8.6 Other Applications

9 Market, By End User

9.1 Introduction

9.2 Pharmaceutical & Biotechnology Companies

9.3 Contract Research Organizations

9.4 Research Centers and Academic & Government Institutes

10 Market, By Region

10.1 Introduction

10.2 North America

10.3 Europe

10.4 Asia Pacific

10.5 Rest of the World

11 Competitive Landscape

11.1 Overview

11.2 Market Share Analysis

11.3 Competitive Leadership Mapping

11.4 Competitive Situation and Trends

12 Company Profiles

12.1 Microsoft Corporation

12.2 NVIDIA Corporation

12.3 IBM Corporation

12.4 Google (A Subsidiary of Alphabet Inc.)

12.5 Atomwise, Inc.

12.6 Deep Genomics

12.7 Cloud Pharmaceuticals, Inc.

12.8 Insilico Medicine

12.9 Benevolentai

12.10 Exscientia

12.11 Cyclica

12.12 Bioage

12.13 Numerate

12.14 Numedii, Inc.

12.15 Envisagenics

12.16 Twoxar, Incorporated

12.17 Owkin, Inc.

12.18 Xtalpi, Inc.

12.19 Verge Genomics

12.20 Berg LLC

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

View source version on businesswire.com: https://www.businesswire.com/news/home/20191213005122/en/

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The Global AI in the Drug Discovery Market is Projected to Reach USD 1,434 Million by 2024 From USD 259 Million in 2019, at a CAGR of 40.8% During the...

DUBLIN, Dec. 13, 2019 /PRNewswire/ -- The "Global Artificial Intelligence (AI) in Agriculture Market: Focus on Product Type (Software, Hardware, AI-as-a-Service), Farming Type (Field Farming, Livestock, Indoor), Application (Crop Protection, Weather Forecasting, Automation), Funding - Analysis and Forecast, 2019-2024" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024.

The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming and rising development of autonomous equipment in agriculture.

Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming. Regions and countries where agriculture is the major source of livelihood and sustenance, artificial intelligence technology has led to greater profitability in the farms of those economies.

The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries. Such favorable advantages associated with the technology have led to extensive investments by all types of stakeholders including government, private investors, corporations, and academic institutions, from across the world.

Expert Quote

Artificial intelligence has become the leader of deep technologies in the era of precision agriculture. It has created the widest impact across agricultural sectors including crop and livestock over recent years. Governments of the majority of the leading countries in the agriculture market are working on their respective national AI strategies. This technology has fastened the digital transformation process, even in sluggish agricultural economies. Its capability to enable precision and autonomy in farm operations has especially caught the attention of growers across the world.

Scope of the Report

The global artificial intelligence in agriculture market research provides a detailed perspective regarding the adoption of AI technology in the agriculture industry, its market size in value, its estimation, and forecast, among others. The purpose of this market analysis is to examine the outlook of artificial intelligence technology in the agriculture industry in terms of factors driving the market, trends, developments, and regulatory landscape, among others.

The report further takes into consideration the funding and investment landscape, government initiatives landscape, market dynamics, and the competitive landscape, along with the detailed financial and product contributions of the key players operating in the market. The artificial intelligence in the agriculture market report is a compilation of different segments including market breakdown by product offering, farming type, application, and region.

Market Segmentation

The global artificial intelligence in the agriculture market (on the basis of product offering) is segmented into software, hardware, AI-as-a-Service, and support services. The software segment dominated the global artificial intelligence in the agriculture market in 2018 and is anticipated to maintain its dominance in market size throughout the forecast period (2019-2024) with hardware and AI-as-a-Service experiencing higher growth rates.

The global artificial intelligence in the agriculture market (on the basis of farming type) is segmented into field farming, livestock farming, indoor farming, and other farming types such as aquaculture. The field farming segment dominated the global artificial intelligence in the agriculture market in 2018 and is anticipated to maintain its dominance throughout the forecast period (2019-2024).

The global artificial intelligence in the agriculture market (on the basis of application) is segmented into crop protection, weather forecasting, precision farming, farm machinery automation, crop growth assessment, and other applications under the category crop, fruit, and vegetable farming. The market is also segmented into animal growth monitoring, animal health monitoring, and other applications under the category livestock and aquaculture farming. The crop protection segment dominated the global artificial intelligence in agriculture market in 2018. Applications such as farm machinery automation and precision farming (across crop and livestock) are anticipated to experience higher growth rates over the forecast period (2019-2024).

The global artificial intelligence in the agriculture market by region is segregated under four major regions, namely North America, Europe, APAC, and Rest-of-the-World. Data for each of these regions has been provided by country. Interesting regional market dynamics have also been provided in the report.

Key Companies in the Global Artificial Intelligence in Agriculture Market

The key market players in the global artificial intelligence in agriculture market include Alibaba Group Holding Limited, AgEagle Aerial Systems Inc., BASF SE, The Climate Corporation (A Bayer AG Company), Deere & Company, IBM Corporation, JD.com Inc., Microsoft Corporation, Robert Bosch GmbH, SAP SE, Connecterra B.V., Descartes Labs, Gamaya SA, Granular Inc., Harvest Croo Robotics, PrecisionHawk, Prospera Technologies Ltd., Root AI Inc., SZ DJI Technology Co. Ltd., Vineview, AGCO Corporation, Capgemini SE, Cargill Inc., CNH Industrial N.V., Iteris Inc., Lindsay Corporation, Abundant Robotics Inc., aWhere Inc., Aquabyte Inc., Ceres Imaging, Delair, ecoRobotix Ltd., Farmers Edge, Taranis, and XAG Co. Ltd., among others.

Key Topics Covered

Executive Summary

1 Market Dynamics1.1 Overview1.2 Impact Analysis1.3 Market Drivers1.3.1 Growing Need for Precision and Efficiency in Agricultural Operations1.3.2 Emerging Complexities in Data-Driven Farming1.3.3 Rising Demand for Autonomous Equipment1.4 Market Restraints1.4.1 Data Privacy Concerns Among Farmers1.4.2 Lack of Technical Infrastructure in Developing Countries1.5 Market Opportunities1.5.1 Favorable Government Initiatives to Support AI in Agriculture1.5.2 Increase in Implementation of Robots and Drones in Agriculture1.5.3 Rise in Adoption of SaaS Business Model in Agriculture

2 Competitive Insights2.1 Key Strategies and Developments2.1.1 Partnerships, Collaborations, and Joint Ventures2.1.2 Product Launches and Developments2.1.3 Business Expansions and Contracts2.1.4 Mergers and Acquisitions2.1.5 Others (Awards and Recognition)2.2 Competitive Benchmarking of Agricultural AI Analytics Companies

3 Industry Analysis3.1 Artificial Intelligence in Agriculture: Technology Ecosystem3.1.1 AI Technology Stack3.1.1.1 AI-Powered Technologies3.1.1.1.1 Machine Learning3.1.1.1.2 Computer Vision3.1.1.1.3 Deep Learning3.1.1.1.4 Speech Recognition Technology3.1.1.1.5 Other Technologies3.1.1.2 Hardware3.1.1.2.1 Memory3.1.1.2.2 Storage3.1.1.2.3 Logic3.1.1.2.4 Networking3.1.1.3 Others3.1.1.4 AI Technology Classifications3.1.1.4.1 AI Technology (by Functionality)3.1.1.4.1.1 Reactive Machines3.1.1.4.1.2 Limited Memory3.1.1.4.1.3 Theory of Mind3.1.1.4.1.4 Self-Awareness3.1.1.4.2 AI Technology (by Capability)3.1.1.4.2.1 Weak AI3.1.1.4.2.2 General AI3.1.1.4.2.3 Strong AI3.1.2 Key AI Use Cases in Agriculture3.1.2.1 Predictive Analytics3.1.2.2 Drones / UAVs3.1.2.3 Robotics3.1.2.4 Autonomous Vehicles3.2 Key Consortiums and Associations3.3 Investment and Funding Landscape3.4 Government Initiatives Landscape3.4.1 North America3.4.2 Europe3.4.3 Asia-Pacific3.4.4 Rest-of-the-World

4 Global Artificial Intelligence in Agriculture Market (by Product Offering), $Million4.1 Assumptions and Limitations for Analysis and Forecast of the Global Artificial Intelligence in Agriculture Market4.2 Market Overview4.3 Software4.4 Hardware4.5 Artificial Intelligence-as-a-Service (AIaaS)4.6 Support Services

5 Global Artificial Intelligence in Agriculture Market (by Farming Type), $Million5.1 Market Overview5.2 Field Farming5.3 Indoor Farming5.4 Livestock Farming5.5 Others

6 Global Artificial Intelligence in Agriculture Market (by Application), $Million6.1 Market Overview6.2 Crops, Fruits, Vegetables, and Other Plants6.2.1 Crop Protection6.2.2 Weather Forecasting6.2.3 Precision Farming6.2.4 Farm Machinery Automation6.2.5 Crop Growth Assessment6.2.6 Others6.3 Livestock and Aquaculture6.3.1 Animal Growth Monitoring6.3.2 Animal Health Monitoring6.3.3 Others

7 Global Artificial Intelligence in Agriculture Market (by Region), $Million7.1 Market Overview7.2 North America7.3 Europe7.4 Asia-Pacific7.5 Rest-of-the-World (RoW)

8 Company Profiles8.1 OverviewPublic CompaniesExisting Market Players8.2 Alibaba Group Holding Limited8.3 AgEagle Aerial Systems Inc.8.4 BASF SE8.5 The Climate Corporation (a Bayer AG Company)8.6 Deere & Company8.7 IBM Corporation8.8 JD.com, Inc.8.9 Microsoft Corporation8.10 Robert Bosch GmbH8.11 SAP SEEmerging Market Players8.12 AGCO Corporation8.13 Capgemini SE8.14 Cargill, Inc.8.15 CNH Industrial N.V.8.16 Iteris, Inc.8.17 Lindsay CorporationPrivate PlayersExisting Market Players8.18 Connecterra B.V.8.19 Descartes Labs, Inc.8.20 Gamaya SA8.21 Granular Inc.8.22 Harvest Croo Robotics, LLC8.23 PrecisionHawk Inc.8.24 Prospera Technologies Ltd.,8.25 Root AI, Inc.8.26 SZ DJI Technology Co. Ltd8.27 VineViewEmerging Market Players8.28 Abundant Robotics Inc.8.29 Aquabyte, Inc.8.30 aWhere Inc.8.31 Ceres Imaging Inc.8.32 Delair8.33 ecoRobotix Ltd.8.34 Farmers Edge8.35 Taranis Ag8.36 XAG Co., Ltd.

For more information about this report visit https://www.researchandmarkets.com/r/b12loy

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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Research and Markets Laura Wood, Senior Manager press@researchandmarkets.com

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AI in Agriculture | Worldwide Markets to 2024: Favorable Government Initiatives & Rise in Adoption of SaaS Business Model Present Opportunities -...

When you think of Artificial Intelligence, you think of all the great technological advancements that are outperforming humans at almost all tasks. This is great, but a lot of AI apps have emerged in the market today that make you question the societal and human impact of this highly-advanced technology. Speaking of which, a group of prominent researchers are alarmed by the harmful societal impact of AI and called for a ban on automated analysis of facial expressions in hiring and other related decisions, reports Reuters.

SEE ALSO: Facebook Has Made An Artificial Intelligence Assistant For Minecraft

As an example of problematic and harmful AI, researchers cited example of the company HireVue, that sells systems for remote video interviews of employers like Hilton and Unilever. HireVue is an AI-based app that analyzes facial movements, tone of voice, speech patterns without disclosing the score to the candidates who have applied for the job. In fact, the nonprofit Electronic Privacy Information Center has also filed a complaint about HireVue to the U.S. FTC.

AI Now, a New-York-based research institute released its fourth annual report on the effects of artificial intelligence tools, where it mentioned that job screening is one of the ways where these kind of software is used without accountability and favors privileged groups. The institute also stated that it wants to take priority action against these software-driven affect recognition, since science doesnt justify its use and there hasnt been widespread adoption of the technology yet. AI Now has also criticized Amazon for its Rekognition software.

HireVue said that it wasnt aware of the AI Now report and didnt respond to any questions surrounding criticism or complaint about the app. Many job candidates have benefited from HireVues technology to help remove the very significant human bias in the existing hiring process, said spokeswoman Kim Paone.

SEE ALSO: Chromes New Feature Uses AI To Describe Images For Blind And Low-Vision Users

A lot of other AI apps have come under scanner over concerns related to harmful use of Artificial Intelligence. For instance, an AI-based babysitter app, Predictim, that uses advanced AI to analyze the risk levels attached to a babysitter. The app gives you a risk score for the babysitter as well as complete details on the babysitter by scanning their social media accounts. Other AI app that received flak recently was the Image-Net Roulette, an online tool that used racist and offensive labels to describe and classify humans.

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AI Software That Analyzes Human Emotions Criticized By Researchers - Mashable India

Reinforcement learning agents or AI thats progressively spurred toward goals via rewards (or punishments) form the foundation of self-driving cars, dexterous robots, and drug discovery systems. But because theyre predisposed to explore unfamiliar states, theyre susceptible to whats called the safe exploration problem, wherein they become fixated on unsafe states (like a mobile robot driving into a ditch, say).

Thats why researchers at Alphabets DeepMind investigated in a paper a method for reward modeling that operates in two phases and is applicable to environments in which agents dont know where unsafe states might be. The researchers say their approach not only successfully trains a reward model to detect unsafe states without visiting them, it can correct reward hacking (loopholes in the reward specification) before the agent is deployed even in new and unfamiliar environments.

Interestingly, their work comes shortly after the release of San Francisco-based research firm OpenAIs Safety Gym, a suite of tools for developing AI that respects safety constraints while training and that compares its safety to the extent it avoids mistakes while learning. Safety Gym similarly targets reinforcement learning agents with constrained reinforcement learning, a paradigm that requires AI systems to make trade-offs to achieve defined outcomes.

The DeepMind teams approach encourages agents to explore a range of states through hypothetical behaviors generated by two systems: a generative model of initial states and a forward dynamics model, both trained on data like random trajectories or safe expert demonstrations. A human supervisor labels the behaviors with rewards, and the agents interactively learn policies to maximize their rewards. Only after the agents have successfully learned to predict rewards and unsafe states are they deployed to perform desired tasks.

Above: DeepMinds safe reinforcement learning approach tested on OpenAI Gym, an environment for AI benchmarking and training.

Image Credit: DeepMind

As the researchers point out, the key idea is the active synthesis of hypothetical behaviors from scratch to make them as informative as possible, without interacting with the environment directly. The DeepMind team calls it reward query synthesis via trajectory optimization, or ReQueST, and explains that it generates four types of hypothetical behaviors in total. The first type maximizes the uncertainty of an ensemble of reward models, while the second and third maximize the predicted rewards (to elicit labels for behaviors with the highest information value) and minimize predicted rewards (to surface behaviors for which the reward model might be incorrectly predicting). As for the fourth category of behavior, it maximizes the novelty of trajectories so as to encourage exploration regardless of predicted rewards.

Finally, once the reward model reaches a satisfactory state, a planning-based agent is deployed one that leverages model-predictive control (MPC) to pick actions optimized for the learned rewards. Unlike model-free reinforcement learning algorithms that learn through trial and error, this MPC enables agents to avoid unsafe states by using the dynamics model to anticipate actions consequences.

To our knowledge, ReQueST is the first reward modeling algorithm that safely learns about unsafe states and scales to training neural network reward models in environments with high-dimensional, continuous states, wrote the coauthors of the study. So far, we have only demonstrated the effectiveness of ReQueST in simulated domains with relatively simple dynamics. One direction for future work is to test ReQueST in 3D domains with more realistic physics and other agents acting in the environment.

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DeepMind proposes novel way to train safe reinforcement learning AI - VentureBeat

UW Computer Science Chair Hank Levy speaks at the TechAliance AI Policy Matters Summit. (GeekWire Photo / Monica Nickelsburg)

About eight years ago, leaders of the University of Washingtons computer science department decided to zero in on artificial intelligence. The goal was to recruit machine learning and AI superstars to lead the department into this new frontier of technology, according to UW Computer Science Chair Hank Levy.

The challenge? Competing for experts with bigger names in academia, like Stanford and MIT.

But the University of Washington had an asset those institutions didnt: Jeff Bezos in its backyard.

So we thought, what the hell? Well send Jeff an email, Levy said Thursday, speaking at the Technlogy Alliances AI Policy Matters Summit in Seattle. The event brought together industry experts, scholars, and elected officials to discuss the state of AI and machine learning and recommend policies to govern the new technology.

UW was trying to recruit the married duo Carlos Guestrin and Emily Fox from Carnegie Mellon University and the University of Pennsylvania, respectively, as well as other tech experts.

Within 24 hours, Bezos responded with two $1 million professorship endowments for Fox and Guestrin. Bezos also stepped in to charm the scholars. He spent a half-hour with them in-person, which Levy said helped seal the deal.

Whether you like Jeff or not, hes very funny, he has the biggest laugh in the world, and hes incredibly impressive, Levy said. It had an impact.

That was the start of a new era for UWs computer science department, Levy said. The department made the front page of the New York Times Sunday business section for its AI efforts in 2012. Microsoft co-founder Paul Allen donated $40 million to create a new computer science department in his name, kicking off several multimillion-dollar rounds of donations from tech companies and leaders. And the new Paul G. Allen school allowed UW to triple its number of computer science majors.

This was the beginning of propelling our department to being one of the very best and best-known in the country, Levy said.

The recruiting efforts also paid unexpected dividends to Apple. Despite his Amazon endowment, the startup that Guestrin spun out of UW was eventually acquired by Apple for $200 million in 2016.

This was the biggest exit out of the department, Levy said. It was a really big deal. One of the reasons that its a big deal is Apple did not have much presence in the region at that point.

Apple is now planning to grow to 2,000 employees at its new Seattle campus in Amazons backyard. Guestrin certainly wont be the last superstar the two tech titans compete over.

The success of Guestrins startup, Turi, benefited UW with more than just prestige. On the eve of the Apple acquisition, Turi gave the UW computer science department a $1 million professor endowment, like the ones Amazon provided to lure Guestrin and Fox.

Remember it was Jeffs and Amazons professorships who helped us to recruit Carlos and Emily and now this company, as it was being acquired by Apple, gave us $1 million to create and another professor in AI, to help us hire the next person in this area, Levy said. So that was an incredible thing that they did for us.

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How Jeff Bezos personally helped the University of Washington recruit its AI superstars - GeekWire

Latent Logic uses "imitation learning" to create simulations of human behavior which can be used in vehicle testing. Most AI training uses reinforcement learning, in which an AI gives answers to problems that are coded as either correct or incorrect. Over time, reinforcement-based AI can learn the correct answer more quickly.

However, this can be rather inefficient. By contrast, imitation learning has machines mimic human behaviors to learn some of the implicit knowledge that people have about the world, making it faster for the AI to model the optimal solution. Waymo could use this technique to train autonomous vehicles by having AI model complex human behaviors like cars cutting each other off or a pedestrian appearing in an unexpected location.

Latent Logic is based in Oxford, UK, which is something of a hub for self-driving vehicle research. For example, there's Oxbotica, a group which has trialed an autonomous grocery delivery vehicles, self-driving taxis and driverless shuttles. BAE Systems worked with researchers in Oxford to develop a hefty off-world autonomous vehicle based on a Bowler Wildcat. There's also the University of Oxford, which performs research into autonomous vehicles as well.

Acquiring the company gives Alphabet a foothold in a key location in the UK and access to a hub of local talent. "We see an exciting opportunity in Europe, not only in continuing to build our partnerships with major automakers but also in benefitting from the world-class technology and engineering capabilities in Oxford and beyond," Drago Anguelov, Waymo's principal scientist and head of research told The Guardian.

Waymo does not plan to launch self-driving car services in the UK yet, but the company has confirmed it has plans to operate in Europe in the future.

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Waymo enters the UK with acquisition of self-driving AI startup Latent Logic - Engadget

AIT News Desk 13 Dec 2019 Computing, News Comments Off on Observe.AI Raises $26 Million to Digitally Transform the $300 Billion Voice Customer Service Market with Artificial Intelligence 52 Views

Company Also Announces a Relationship with Microsoft to Bring AI-Powered Coaching and Conversational Insights to Joint Customers

Observe.AI, the leader in AI-powered agent enablement for voice customer service, announced a $26 million Series A financing led by Scale Venture Partners, with participation from Nexus Venture Partners, Steadview Capital, 01 Advisors, and Emergent Ventures. This funding allows Observe.AI to expand its US-India team globally and accelerate product development.

In conjunction with the funding, Andy Vitus, partner at Scale, will be joining Observe.AIs board. This brings the companys total funding to $34 million.

Legacy speech analytics systems are simply not meeting the needs of the worlds top brands, said Swapnil Jain, CEO and co-founder of Observe.AI. Todays customer service agents have a unique ability to emotionally connect with customers and are often a brands only frontline representatives. This investment will fuel our mission to elevate agent performance through AI-based coaching and insights.

Many support teams monitor just 1-2 percent of calls and use three or more systems to access conversational insights and enable agents. Observe.AI uses the latest speech, natural language processing, and deep learning technologies to analyze 100 percent Observe.AI of customer conversations and provide adaptive coaching, including completely automating some parts of the quality assurance and compliance tracking processes. The platform becomes smarter with each call analysis.

Read More: ImmersiveTouch Launches New Personalized VR Imaging Platform into the Radiology Market

Observe.AI is already disrupting the $300 billion voice customer service market by rethinking how agents are coached and the way top brands provide personalized customer experiences, said Andy Vitus, Partner at Scale Venture Partners.

Observe.AI also announced that it has been accepted into the Microsoft for Startups program. With this relationship, Microsoft customers can leverage Observe.AIs platform through its Azure marketplace.

At Microsoft, were thrilled to see one of our Microsoft for Start-Up members excel Observe.AI as one of the fastest-growing startups in the Bay Area. Observe.AI continues to define how AI can transform the customer experience, impacting enterprise support teams to improve quality of service, agent performance, and productivity, said Shaloo Garg, Managing Director, Microsoft for Start-Ups.

Read More: AiThority Interview with Robert Cruz, Senior Director of Information Governance at Smarsh

In the past 12 months, Observe.AI has signed 100 customers and formed partnerships with leading organizations like Microsoft, Talkdesk, ERCBPO, and itelBPO. Some of the worlds largest enterprises and emerging brands use Observe.AI, including TripAdvisor, Concentrix, ClearMe, and Root Insurance. Thousands of global agents are coached with Observe.AI, which provides a detailed look at how top agents successfully structure calls so those tactics can be replicated.

We expect to see a 4X increase in annual recurring revenue in 2020, said Sharath Keshava Narayana, CRO of Observe.AI. With plans to significantly expand our sales, marketing, and customer success teams over the next few months, were both eager and grateful to build on the momentum.

Observe.AI is set to transform voice customer service for the AI era. We are delighted to have partnered with them from the early days of their journey, said Jishnu Bhattacharjee, Nexus Managing Director and Observe.AI board member.

Read More: OptimalPlus Opens German Office and Partners with ZF

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Observe.AI Raises $26 Million to the $300 Billion Voice Customer Service - AiThority

Intelligence is moving to the edge. As the growth of data acquisition accelerates, migration of compute closer to the edge allows more efficient use of data at reduced latency and infrastructure cost. Computer visionis a field of artificial intelligence that trainsmachinesto interpret and understand the visual world.

One of the goals ofcomputer visionis for machines to see and process images in the same way humans do. Computer vision through machine learning uses a method called supervised learning where a large annotated image set is used to construct a computational model. While model training can be intense and time-consuming, once trained, this model can quickly and effectively perform a variety of tasks.

Some examples of image and video analysis include identifying, classifying, counting and estimating pose of objects; compressing and encoding visual information for transmission or matching; estimating camera perspective; 2-D segmentation of foreground and background; estimating depth and performing 3-D segmentation; and inpainting or inferring visual data for occluded regions of an image.

With the recent technological advancement of edge AI, the ability to process and analyze data locally at the camera as opposed to streaming data to the cloud means that computer vision may be at the forefront of leveraging the wood pallet industry.

Three types of computer vision architecture exist: video or images that are sent to the cloud for computation; partial computing on the edge where only a few modalities are transferred to the cloud to search, sort and compute; and edge AI, which computes all image data on the edge. The latter requires training a model to be installed on the edge and updated at frequent intervals. These architectures have unique advantages and disadvantages. With the growing body of computer vision research, it is possible to find a model that is well suited to each application.

Pallets transport goods throughout the world and are an integral part of the supply chain logistics industry. The European Federation of Wooden Pallet and Packaging Manufacturers (FEFPEB) reported that more than 3 billion wooden pallets are in circulation in the EU, while nearly 2 billion wooden pallets are used each day in the U.S. The majority of these pallets are owned by large pooling solution companies (such as my company), which lease pallets from a shared pool, reducing complexity of pallets procurement, management and recovery for companies managing the supply chain of their products.

Pooling solution companies want to monitor pallet movement throughout the supply chain to understand pallet losses and recovery, pallet damage and pallet cycle time. To achieve comprehensive monitoring, each pallet could be labeled with a unique identifier or a tracking device. If a pallet has a unique ID such as a barcode or a QR code, computer vision can be used to track the pallet as it flows through the supply chain. If a pallet is instrumented with a tracking device, it can be detected through computer vision for device replacement or maintenance as the pallet flows through the sortation process in a service center.

Pallets are made from a wide variety of wood, including beech, ash, poplar, pine and spruce. The type of wood the pallets are made from forms the unique composition of wood tissue contours that not only provide insight into how strong and durable a single pallet can be, but can also provide a unique identifier for a pallet that can be used for tracking. Additionally, the footprint of nails that fasten the wooden boards with blocks form a topology that can provide additional information about the pallet life cycle, such as how long the pallet will last in the supply chain before it hits the repair belt.

Through well-trained computer vision models, the unique grain patterns of each pallet can be identified at birth, and this identity can be managed as the pallet flows through its life cycle. Changes to the wood patterns and structure can be tracked as the pallet is damaged and repaired as it cycles through the supply chain. Computer vision will not only allow tracking of pallets by just images, but it will also give insights into pallet strength and durability. This low-cost solution equips pallet companies to take action to filter unreliable pallets at birth or after repeated use. Furthermore, pallet logistics companies can gather insights into the number of damages and types of damages by customer and industry verticals to enhance the business model and improve the design to make the platforms more rugged.

With 95% of organizations and institutions reporting their continued use, wooden pallets continue to dominate the supply chain market. Wood is the only material that is 100% renewable, recyclable, reusable and rated for hygienic transport several classes of goods.

Computer vision can also play a crucial role in maintaining an accurate inventory count of pallets in any warehouse. From an image that contains one or more stacks of pallets, a well-trained neural network can produce a count of all pallets in less time and with more accuracy than a well-trained eye.

Beyond supply chain KPIs, computer vision can be applied to both worker safety and efficiency within the service centers where pallets are stored, inspected and repaired. Any repetitive task, such as a nailing activity on a repair bench, can be fed into a model to identify worker accuracy, fatigue and many more actions from a live video stream. Additionally, human detection can be used to identify when people might be present in unauthorized or hazardous areas of a facility and if they are wearing appropriate PPE. This can help prevent workplace accidents, which is the highest priority in this business.

Excerpt from:

Confluence Of AI On The Edge And Computer Vision In The Wood Pallets Industry - Forbes

Photo: Artem Peretiatko | Getty Images

Why We Need More Women in STEM and How AI Could Help Us Get There

Recently, Dr. France A. Crdova, director of the National Science Foundation, gave a presentation at the U.S. Council on Competitiveness meeting in Washington, D.C. She holds an extraordinary record of accomplishment and has made a tremendous impact on academia and the U.S.'s scientific community. Crdova is also the youngest person and first woman to serve as Chief Scientist at NASA. Her journey began with her love for STEM.

In some ways, the future of workis largely linked to STEM. Yet in this day and age, despite role models like Crdova, women continue to remain significantly underrepresented across the board in this industry. Whats more, the inevitable reality of an AI-integrated workforce is coming.

According to recent research, women make up just 26 percentof those who hold computer- and math-related jobs. Moreover, data from UNESCO indicates that only 35 percentof women go into STEM, of which a mere 3 percentdecided to pursue fields like IT. And when it comes to computer science degrees in the U.S.,only 18 percentofthem are earned by female college graduates.

Interestingly, in Eastern Europe, more women tend to pursue STEM. About 74 percent of women occupy medical professions in countries like Estonia and Latvia. In Bulgaria, 27 percentof IT workers are women a nine-fold lead over the U.S. AndEastern European countries boast the highest proportion of women who work in high-tech companies.

These results are partly a product of socialist-era policies that encouragedfemales to pursue the maths and sciences in the name of the States advancement. American can learn from the policy initiative of driving the populace to pursue STEM via large-scale campaigns for societal betterment.

Though Eastern Europe trumps America when it comes to women in STEM, many females who do choose to explore the industry report experiencing gender discrimination. Results from a survey of 1,000 college-aged women conducted by Girls Who Code suggested that "half [of the women] had either had a negative experience applying for a job in tech or know a woman who has. Furthermore, of the survey respondents that reported a negative encounter, 25 percentsaid their interviewers focused on their personal attributes rather than their skills and 21 percentof women said they encountered biased questions.

In light of this pressing issue, female-centric STEM initiatives has appeared across the US. Among the best known national programs include the previously mentioned Girls Who Code organization, as well as Kode With Klossy, run by former supermodel Karlie Kloss. And though specialized STEM programs for girls are a step in the right direction, we need to make a leap. Current efforts arent nearly comprehensive enough to adequately prepare women for an AI-augmented reality and work towards solving the problem of discrimination and the gender gap.

We can take advantage of the inescapable marriage of technology and biology to craft a novel multi-part solution for helping solve the discrimination and STEM gender gap:

Women make up just under half (47 percent) of the workforce, but they are 58 percent of workers at the highest risk of automation, states a recent report by the Institute for Womens Policy Research. Therefore, the digital workforce revolution could drive some women to encounter high levels of job insecurity.

But just how much of a threat does automation pose? According to the U.S.Census, between 2006 and 2010, 96 percentof secretarial and administrative positions were occupied by women. Females also reportedly hold77 percentof teaching positions and 78 percentof central-office administrator roles.

Learning from eastern Europe, America can introduce STEM awareness campaigns, large-scale private-public initiatives through whichthe government, academic and private institutions work in tandem to educate the public about STEM. Oneexample of a current STEM awareness initiative is STEMFuture, an international non-profit organization that provides education and workshops for adolescents to encourage careers in technology, mathand science.

STEM awareness campaigns have the potential to significantly lessen the strain of automation on womenand deliver a new set of opportunities and benefits to the female workforce of tomorrow.

Scientific research suggeststhe female brain matures faster than the male brainand possesses unique structural attributes. For example, girls tend to have stronger neural networks in the temporal lobe, leading to better memorization and listening abilities. In addition, the corpus callosum (a weave of fibers that conjoin the left and right hemispheres of the brain), can be up to 25 percentlarger in developing female adolescents than in their male counterparts.

Currently, schools teach boys and girls at an equivalent pace, neglecting their separate biological needs. If educators take advantage of development differences, special STEM curriculums could be crafted for girls at an early age. This could help bring STEM to girls across classrooms in the U.S. and encourage them to explore the field more deeply.

Using AI to improve the HR hiring process isnt news. Many companies use AI recruitment software that aims to make hiring more efficient or cut down on bias. This innovation suggests that AI will remain central to the future workplace environment.

According to a Pew Research report, about four in ten working women (42 percent) in the United States say they have faced discrimination on the job because of their gender. Moreover, another another study by Pew suggests that 50 percentof women in STEM jobs have experienced gender discrimination. Carefully-vetted AI couldhelp decrease gender bias discrimination in STEM by exclusively assessing candidates based on skills.

A digital society is a dynamic one. In the future, new technologies will regularly enter the marketplace, continuing to make lifelong learning necessary. A skills-based economy means that degrees and hierarchies will no longer be as relevant. When abilities are prioritized above factors like gender, more women could feel empowered toenter the STEM industry, knowing they'd be less likely to be assessed on the basis of gender.

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Originally posted here:

Why We Need More Women in STEM and How AI Could Help Us Get There - Thehour.com

The evolution from research to product

Its one thing for a Microsoft researcher to use all the available bells and whistles, plus Azures powerful computing infrastructure, to develop an AI-based machine translation model that can perform as well as a person on a narrow research benchmark with lots of data. Its quite another to make that model work in a commercial product.

To tackle the human parity challenge, three research teams used deep neural networks and applied other cutting-edge training techniques that mimic the way people might approach a problem to provide more fluent and accurate translations. Those included translating sentences back and forth between English and Chinese and comparing results, as well as repeating the same translation over and over until its quality improves.

In the beginning, we were not taking into account whether this technology was shippable as a product. We were just asking ourselves if we took everything in the kitchen sink and threw it at the problem, how good could it get? Menezes said. So we came up with this research system that was very big, very slow and very expensive just to push the limits of achieving human parity.

Since then, our goal has been to figure out how we can bring this level of quality or as close to this level of quality as possible into our production API, Menezes said.

Someone using Microsoft Translator types in a sentence and expects a translation in milliseconds, Menezes said. So the team needed to figure out how to make its big, complicated research model much leaner and faster. But as they were working to shrink the research system algorithmically, they also had to broaden its reach exponentially not just training it on news articles but on anything from handbooks and recipes to encyclopedia entries.

To accomplish this, the team employed a technique called knowledge distillation, which involves creating a lightweight student model that learns from translations generated by the teacher model with all the bells and whistles, rather than the massive amounts of raw parallel data that machine translation systems are generally trained on. The goal is to engineer the student model to be much faster and less complex than its teacher, while still retaining most of the quality.

In one example, the team found that the student model could use a simplified decoding algorithm to select the best translated word at each step, rather than the usual method of searching through a huge space of possible translations.

The researchers also developed a different approach to dual learning, which takes advantage of round trip translation checks. For example, if a person learning Japanese wants to check and see if a letter she wrote to an overseas friend is accurate, she might run the letter back through an English translator to see if it makes sense. Machine learning algorithms can also learn from this approach.

In the research model, the team used dual learning to improve the models output. In the production model, the team used dual learning to clean the data that the student learned from, essentially throwing out sentence pairs that represented inaccurate or confusing translations, Menezes said. That preserved a lot of the techniques benefit without requiring as much computing.

With lots of trial and error and engineering, the team developed a recipe that allowed the machine translation student model which is simple enough to operate in a cloud API to deliver real-time results that are nearly as accurate as the more complex teacher, Menezes said.

In the rapidly evolving AI landscape, where new language understanding models are constantly introduced and improved upon by others in the research community, Bings search experts are always on the hunt for new and promising techniques. Unlike the old days, in which people might type in a keyword and click through a list of links to get to the information theyre looking for, users today increasingly search by asking a question How much would the Mona Lisa cost? or Which spider bites are dangerous? and expect the answer to bubble up to the top.

This is really about giving the customers the right information and saving them time, said Rangan Majumder, partner group program manager of search and AI in Bing. We are expected to do the work on their behalf by picking the most authoritative websites and extracting the parts of the website that actually shows the answer to their question.

To do this, not only does an AI model have to pick the most trustworthy documents, but it also has to develop an understanding of the content within each document, which requires proficiency in any number of language understanding tasks.

Last June, Microsoft researchers were the first to develop a machine learning model that surpassed the estimate for human performance on the General Language Understanding Evaluation (GLUE) benchmark, which measures mastery of nine different language understanding tasks ranging from sentiment analysis to text similarity and question answering. Their Multi-Task Deep Neural Network (MT-DNN) solution employed both knowledge distillation and multi-task learning, which allows the same model to train on and learn from multiple tasks at once and to apply knowledge gained in one area to others.

Bings experts this fall incorporated core principles from that research into their own machine learning model, which they estimate has improved answers in up to 26 percent of all questions sent to Bing in English markets. It also improved caption generation or the links and descriptions lower down on the page in 20 percent of those queries. Multi-task deep learning led to some of the largest improvements in Bing question answering and captions, which have traditionally been done independently, by using a single model to perform both.

For instance, the new model can answer the question How much does the Mona Lisa cost? with a bolded numerical estimate: $830 million. In the answer below, it first has to know that the word cost is looking for a number, but it also has to understand the context within the answer to pick todays estimate over the older value of $100 million in 1962. Through multi-task training, the Bing team built a single model that selects the best answer, whether it should trigger and which exact words to bold.

Earlier this year, Bing engineers open sourced their code to pretrain large language representations on Azure. Building on that same code, Bing engineers working on Project Turing developed their own neural language representation, a general language understanding model that is pretrained to understand key principles of language and is reusable for other downstream tasks. It masters these by learning how to fill in the blanks when words are removed from sentences, similar to the popular childrens game Mad Libs.

You take a Wikipedia document, remove a phrase and the model has to learn to predict what phrase should go in the gap only by the words around it, Majumder said. And by doing that its learning about syntax, semantics and sometimes even knowledge. This approach blows other things out of the water because when you fine tune it for a specific task, its already learned a lot of the basic nuances about language.

To teach the pretrained model how to tackle question answering and caption generation, the Bing team applied the multi-task learning approach developed by Microsoft Research to fine tune the model on multiple tasks at once. When a model learns something useful from one task, it can apply those learnings to the other areas, said Jianfeng Gao, partner research manager in the Deep Learning Group at Microsoft Research.

For example, he said, when a person learns to ride a bike, she has to master balance, which is also a useful skill in skiing. Relying on those lessons from bicycling can make it easier and faster to learn how to ski, as compared with someone who hasnt had that experience, he said.

In some sense, were borrowing from the way human beings work. As you accumulate more and more experience in life, when you face a new task you can draw from all the information youve learned in other situations and apply them, Gao said.

Like the Microsoft Translator team, the Bing team also used knowledge distillation to convert their large and complex model into a leaner model that is fast and cost-effective enough to work in a commercial product.

And now, that same AI model working in Microsoft Search in Bing is being used to improve question answering when people search for information within their own company. If an employee types a question like Can I bring a dog to work? into the companys intranet, the new model can recognize that a dog is a pet and pull up the companys pet policy for that employee even if the word dog never appears in that text. And it can surface a direct answer to the question.

Just like we can get answers for Bing searches from the public web, we can use that same model to understand a question you might have sitting at your desk at work and read through your enterprise documents and give you the answer, Majumder said.

Top image: Microsoft investments in natural language understanding research are improving the way Bing answers search questions like How much does the Mona Lisa cost? Image by Muse du Louvre/Wikimedia Commons.

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Jennifer Langston writes about Microsoft research and innovation. Follow her on Twitter.

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From search to translation, AI research is improving Microsoft products - Microsoft