While, in the past, artificial intelligence may have been thought to be a product of science fiction, most professionals today understand that the adoption of smart technology is actively changing workplaces. There are applications of AI throughout nearly every profession and industry, and human resources careers are no exception.

A recent survey conducted by Oracle and Future Workplace found that human resources professionals believe AI can present opportunities for mastering new skills and gaining more free time, allowing HR professionals to expand their current roles in order to be more strategic within their organization.

Among HR leaders who participated in the survey, however, 81 percent said that they find it challenging to keep up with the pace of technological changes at work. As such, it is more important now than ever before for human resources professionals to understand the ways in which AI is reshaping the industry.

Read on to explore what artificial intelligence entails, how it is applied to the world of human resources management, and how HR professionals can prepare for the future of the field today.

At a high level, artificial intelligence (AI) is a technology that allows computers to learn from and make or recommend actions based on previously collected data. In terms of human resources management, artificial intelligence can be applied in many different ways to streamline processes and improve efficiency.

Uwe Hohgrawe, lead faculty for Northeasterns Master of Professional Studies in Analytics program explains that we as humans see the information in front of us and use our intelligence to draw conclusions. Machines are not intelligent, but we can make them appear intelligent by feeding them the right information and technology.

Learn More: AI & Other Trends Defining the HRM Industry

While organizations are adopting AI into their human resources processes at varying rates, it is clear to see that the technology will have a lasting impact on the field as it becomes more widely accepted. For this reason, it is important that HR professionals prepare themselves for these changes by understanding what the technology is and how it is applied across various functions.

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Among the numerous applications of AI in the human resources sector, some of the first changes HR professionals should expect to see involve recruitment and onboarding, employee experience, process improvement, and the automation of administrative tasks.

While many organizations are already beginning to integrate AI technology into their recruiting efforts, the vast majority of organizations are not. In fact, Deloittes 2019 Global Human Capital Trends survey found that only 6 percent of respondents believed that they had the best-in-class recruitment processes in technology, while 81 percent believed their organizations processes were standard or below standard. For this reason, there are tremendous opportunities for professionals to adapt their processes and reap the benefits of using this advanced technology.

During the recruitment process, AI can be used to the benefit of not only the hiring organization but its job applicants, as well. For example, AI technology can streamline application processes by designing more user-friendly forms that a job applicant is more likely to complete, effectively reducing the number of abandoned applications.

While this approach has made the role of the human resources department in recruitment much easier, artificial intelligence also allows for simpler and more meaningful applications on the candidates end, which has been shown to improve application completion rates.

Additionally, AI has played an important role in candidate rediscovery. By maintaining a database of past applicants, AI technology can analyze the existing pool of applicants and identify those that would be a good fit for new roles as they open up. Rather than expending time and resources searching for fresh talent, HR professionals can use this technology to identify qualified employees more quickly and easily than ever before.

Once hiring managers have found the best fit for their open positions, the onboarding process begins. With the help of AI, this process doesnt have to be restricted to standard business hoursa huge improvement over onboarding processes of the past.

Instead, AI technology allows new hires to utilize human resources support at any time of day and in any location through the use of chatbots and remote support applications. This change not only provides employees with the ability to go through the onboarding process at their own pace, but also reduces the administrative burden and typically results in faster integration.

In addition to improvements to the recruitment process, HR professionals can also utilize artificial intelligence to boost internal mobility and employee retention.

Through personalized feedback surveys and employee recognition systems, human resources departments can gauge employee engagement and job satisfaction more accurately today than ever before. This is incredibly beneficial considering how important it is to understand the overall needs of employees, however there are several key organizational benefits to having this information, as well.

According to a recent report from the Human Resources Professional Association, some AI software can evaluate key indicators of employee success in order to identify those that should be promoted, thus driving internal mobility. Doing so has the potential to significantly reduce talent acquisition costs and bolster employee retention rates.

This technology is not limited to identifying opportunities to promote from within, however; it can also predict who on a team is most likely to quit. Having this knowledge as soon as possible allows HR professionals to deploy retention efforts before its too late, which can strategically reduce employee attrition.

One of the key benefits of leveraging artificial intelligence in various human resources processes is actually the same as it is in other disciplines and industries: Automating low value, easily repeatable administrative tasks gives HR professionals more time to contribute to strategic planning at the organizational level. This, in turn, enables the HR department to become a strategic business partner within their organizations.

Smart technologies can automate processes such as the administration of benefits, pre-screening candidates, scheduling interviews, and more. Although each of these functions is important to the overall success of an organization, carrying out the tasks involved in such processes is generally time-consuming, and the burden of these duties often means that HR professionals have less time to contribute to serving their employees in more impactful ways.

Deploying AI software to automate administrative tasks can ease this burden. For instance, a study by Eightfold found that HR personnel who utilized AI software performed administrative tasks 19 percent more effectively than departments that do not use such technology. With the time that is saved, HR professionals can devote more energy to strategic planning at the organizational level.

While it is clear that artificial intelligence will continue to positively shape the field of human resources management in the coming years, HR professionals should also be aware of the challenges that they might face.

The most common concerns that HR leaders have focus primarily on making AI simpler and safer to use. In fact, the most common factor preventing people from using AI at work are security and privacy concerns. Additionally, 31 percent of respondents in Oracles survey expressed that they would rather interact with a human in the workplace than a machine. Moving forward, HR professionals will need to be prepared to address these concerns by staying on top of trends and technology as they evolve and change.

People will need to be aware of ethical and privacy questions when using this technology, Hohgrowe says. In human resources, [AI] can involve using sensitive information to create sensitive insights.

For instance, employees want their organizations to respect their personal data and ask for permission before using such technology to gather information about them. However organizations also want to feel protected from data breaches, and HR professionals must take the appropriate security measures into account.

To prepare for the future of human resources management, professionals should take the necessary steps to learn about current trends in the field, as well as lay a strong foundation of HR knowledge that they can build upon as the profession evolves.

Staying up to date with industry publications and networking with leaders in the field is a great way to stay abreast of current trends like the rapid adoption of artificial intelligence technologies. Building your foundational knowledge of key human resource management theories, strategy, and ethics, on the other hand, is best achieved through higher education.

Although there are many certifications and courses available that focus on specific HR topics, earning an advanced degree like a Master of Science in Human Resources Management provides students with a more holistic approach to understanding the connection between an organization and its people.

At Northeastern, we highlight the importance of three literacies: data literacy, technological literacy, and humanic literacy. That combination is one of the areas where I believe we will pave the way in the future, Hohgrawe says. This also allows us to explore augmented artificial intelligence in a way that appreciates the relationship between human, machine, and data.

Students looking to specialize in AI also have the opportunity to declare a concentration in artificial intelligence within Northeasterns human resource management program. Those who specialize in this specific aspect of the industry will study topics such as human resources information processing, advanced analytical utilization, and AI communication and visualization. Similarly, those who seek a more technical masters degree might consider a Northeasterns Master of Professional Studies in Enterprise Intelligence, which also includes a concentration in AI for human resources.

No matter each students specific path, however, those who choose to study at Northeastern will have the unique chance to learn from practitioners with advanced knowledge and experience in the field. Many of Northeasterns faculty have previously or are currently working in the human resources management field, enabling them to bring a unique perspective to the classroom and educate students on the real-world challenges that HR professionals face today.

Between the world-class faculty members and the multitude of experiential learning opportunities provided during the pursuit of a masters degree, aspiring HR professionals will graduate from Northeasterns program with the unique combination of experience and expertise needed to land a lucrative role in this growing field.

Interested in advancing your career in HR? Explore Northeasterns Master of Science in Human Resources Management program and consider taking the next step toward a career in this in-demand industry.

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Artificial Intelligence in Human Resource Management

Artificial Intelligence has facilitated the processing of a large amount of data and its use in the industry. The number of tools and frameworks available to data scientists and developers has increased with the growth of AI and ML. This article on Artificial Intelligence Tools & Frameworks will list out some of these in the following sequence:

Development of neural networks is a long process which requires a lot of thought behind the architecture and a whole bunch of nuances which actually make up the system.

These nuances can easily end up getting overwhelming and not everything can be easily tracked. Hence, the need for such tools arises, where humans handle the major architectural decisions leaving other optimization tasks to such tools. Imagine an architecture with just 4 possible booleanhyperparameters, testing all possible combinations would take 4! Runs. Retraining the same architecture 24 times is definitely not the best use of time and energy.

Also, most of the newer algorithms contain a whole bunch of hyperparameters. Heres where new tools come into the picture. These tools not only help develop but also, optimize these networks.

From the dawn of mankind, we as a species have always been trying to make things to assist us in day to day tasks. From stone tools to modern day machinery, to tools for making the development of programs to assist us in day to day life. Some of the most important tools and frameworks are:

Scikit-learn is one of the most well-known ML libraries. It underpins many administered and unsupervised learning calculations. Precedents incorporate direct and calculated relapses, choice trees, bunching, k-implies, etc.

It includes a lot of calculations for regular AI and data mining assignments, including bunching, relapse and order. Indeed, even undertakings like changing information, feature determination and ensemble techniques can be executed in a couple of lines.

For a fledgeling in ML, Scikit-learn is a more-than-adequate instrument to work with, until you begin actualizing progressively complex calculations.

On the off chance that you are in the realm of Artificial Intelligence, you have most likely found out about, attempted or executed some type of profound learning calculation. Is it accurate to say that they are essential? Not constantly. Is it accurate to say that they are cool when done right? Truly!

The fascinating thing about Tensorflow is that when you compose a program in Python, you can arrange and keep running on either your CPU or GPU. So you dont need to compose at the C++ or CUDA level to keep running on GPUs.

It utilizes an arrangement of multi-layered hubs that enables you to rapidly set up, train, and send counterfeit neural systems with huge datasets. This is the thing that enables Google to recognize questions in photographs or comprehend verbally expressed words in its voice-acknowledgment application.

Theano is wonderfully folded over Keras, an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras fundamental favorable position is that it is a moderate Python library for profound discovering that can keep running over Theano or TensorFlow.

What sets Theano separated is that it exploits the PCs GPU. This enables it to make information escalated counts up to multiple times quicker than when kept running on the CPU alone. Theanos speed makes it particularly profitable for profound learning and other computationally complex undertakings.

Caffe is a profound learning structure made with articulation, speed, and measured quality as a top priority. It is created by the Berkeley Vision and Learning Center (BVLC) and by network donors. Googles DeepDream depends on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.

It allows for trading computation time for memory via forgetful backprop which can be very useful for recurrent nets on very long sequences.

If you like the Python-way of doing things, Keras is for you. It is a high-level library for neural networks, using TensorFlow or Theano as its backend.

The majority of practical problems are more like:

In all of these, Keras is a gem. Also, it offers an abstract structure which can be easily converted to other frameworks, if needed (for compatibility, performance or anything).

PyTorch is an AI system created by Facebook. Its code is accessible on GitHub and at the present time has more than 22k stars. It has been picking up a great deal of energy since 2017 and is in a relentless reception development.

CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK is available for anyone to try out, under an open-source license.

Out of all the tools and libraries listed above, Auto ML is probably one of the strongest and a fairly recent addition to the arsenal of tools available at the disposal of a machine learning engineer.

As described in the introduction, optimizations are of the essence in machine learning tasks. While the benefits reaped out of them are lucrative, success in determining optimal hyperparameters is no easy task. This is especially true in the black box like neural networks wherein determining things that matter becomes more and more difficult as the depth of the network increases.

Thus we enter a new realm of meta, wherein software helps up build software. AutoML is a library which is used by many Machine learning engineers to optimize their models.

Apart from the obvious time saved, this can also be extremely useful for someone who doesnt have a lot of experience in the field of machine learning and thus lacks the intuition or past experience to make certain hyperparameter changes by themselves.

Jumping from something that is completely beginner friendly to something meant for experienced developers, OpenNN offers an arsenal of advanced analytics.

It features a tool, Neural Designer for advanced analytics which provides graphs and tables to interpret data entries.

H20 is an open-source deep learning platform. It is an artificial intelligence tool which is business oriented and help them to make a decision from data and enables the user to draw insights. There are two open source versions of it: one is standard H2O and other is paid version Sparkling Water. It can be used for predictive modelling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence.

Google ML Kit, Googles machine learning beta SDK for mobile developers, is designed to enable developers to build personalised features on Android and IOS phones.

The kit allows developers to embed machine learning technologies with app-based APIs running on the device or in the cloud. These include features such as face and text recognition, barcode scanning, image labelling and more.

Developers are also able to build their own TensorFlow Lite models in cases where the built-in APIs may not suit the use case.

With this, we have come to the end of our Artificial Intelligence Tools & Frameworks blog. These were some of the tools that serve as a platform for data scientists and engineers to solve real-life problems which will make the underlying architecture better and more robust.

You can check out theAI and Deep Learning with TensorFlow Course that is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.

Got a question for us? Please mention it in the comments section of Artificial Intelligence Tools & Frameworks and we will get back to you.

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Top 12 Artificial Intelligence Tools & Frameworks | Edureka

Artificial intelligence and automation have been two hot areas of investment, especially over the past decade. As the worldwide workforce increasingly shifts to a remote workforce, the need for automation, technology, and tools continues to grow. As such, its no surprise that automation and intelligent systems continue to be of significant interest to venture capitalists who are investing in growing firms focused in these areas. The AI Today podcast had the chance to talk to Oliver Mitchell, a Founding Partner of Autonomy Ventures. (disclosure: Im a co-host of the AI Today podcast).

Oliver Mitchell

For over 20 years Oliver has been working on technology startups and in the past decade he has been working on investing in automation. He spoke with us about seeing the big changes that are coming to the world with automation and the exciting possibilities that it still has to offer. He is a partner at venture firm Autonomy Ventures, an early stage venture capital firm that looks to invest in automation and robotics.

The best AI solutions are the ones that solve industry-specific problems

Despite the fact that Artificial Intelligence has been around for decades, there is still no commonly accepted definition. Because of this, artificial intelligence means something different to every industry, and this is reflected in the sort of investments that Oliver and other VCs are seeing. While some technology firms may be focused on how artificial intelligence can better help them manage funds, other companies might be more interested in how AI can supplement their human workforce. The various different tasks that artificial intelligence can help with is something that investors need to look at when making their investments.

Out of all of the investments that Oliver has made over the years, the best ones have been with companies that really focus on solving specific problems in an industry. In particular, applications of robotics to manufacturing, and specifically the concept of collaborative robots is appealing. Collaborative robots can be used to work alongside employees. To make the arm easier to use it has AI onboard and a suite of tools to enable anyone to operate the arm without technological training. With this arm, companies dont need to spend hundreds of thousands of dollars to hire specialists to train their robotic arms. Rather, the arm can be taught through movement how to carry out tasks through an iPad or similar device. This arm falls under the category of collaborative robots, or cobots for short, that are able to work side by side with humans.

About half of the Autonomy Ventures portfolio companies are based out of Israel. One portfolio company is Aurora Labs, which focuses on providing a software platform for autonomous and connected cars to monitor their onboard software. Aurora Labs calls their software a self-healing software for connected cars. Your average car needs to go to a dealership in order to receive any kind of firmware or software update if an issue is detected. This is because the technician needs to plug a device into the OBDII port of the car. Due to limited power in the chips in most current cars, they arent able to access the cloud. Even those cars that have OnStar onboard have very limited connectivity. Self-healing software for connected cars from Aurora Labs allows cars to connect to the cloud so that they can receive updates over the air. While much of this solution isnt AI per se, the use of machine learning for more adaptive updates is part of the indication that AI is finding its application in a wide range of niches.

Keeping AI in check

Something important that Oliver addressed is the view and aims of AI. A lot of people have a science fiction perspective on artificial intelligence. He believes that we need to manage our expectations on AI because there are many tasks that AI still cant do that even a child can. One example Oliver uses is the ability to tie a shoe. While a 7-year-old has been able to tie shoes for years, robots still cannot tie a shoe. We need to be able to address everyday problems before we can start to move on to what we see in movies.

Oliver also is concerned about issues of bias in AI and machine learning, especially as systems become more autonomous. Software around the world is used to help humans but so many of us are quick to turn to technology without a chance to evaluate its proper use. Oliver sites many examples including the AI-based criminal justice system that was biased in its assessment of an offenders likelihood of reoffending. Once the software was deployed in multiple states it was found that it rated people of color more likely to reoffend.

Oliver also points out bias in a type of technology that is used in emergency departments around the world to analyze patients. The software looks at a patients chief complaint, symptoms, and medical history along with demographics and gives the medical staff a recommendation about what to do. However, this software has been found to not take into account the human aspect of medical care. It will make a decision based on a perceived likelihood of effective treatment, not on saving every life possible.

Regardless of the challenges and limitations of AI, investors and entrepreneurs see significant potential for both simple automation and more complicated intelligent and autonomous systems. Companies are continuing to push the boundary of whats possible, especially in our increasingly remote and virtual world. It should be no surprise then that VCs will continue to look to invest in these types of companies as AI becomes part of our every day lives.

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Artificial Intelligence And Automation Top Focus For Venture Capitalists - Forbes

The new CEPEJ Working group on Cyberjustice and artificial intelligence (CEPEJ-GT-CYBERJUST) will hold a first meeting by videoconference on 27 April 2020.

The objective of the Working group is to analyse and develop appropriate tools on new issues such as the use of cyberjustice or artificial intelligence in judicial systems in relation to the efficiency and quality of judicial systems.

At this meeting, an exchange of views will take place on the possible future work of the Working Group, which should be based on the themes contained in its mandate:

The CYBERJUST group will also hold a joint meeting at a later stage with the CEPEJ Working Group on Quality of Justice (CEPEJ-GT-QUAL) with a view to sharing tasks, in particular to follow up the implementation of the CEPEJ European Ethical Charter on the use of artificial intelligence in judicial systems and their environment and its toolbox and to ensure co-ordination.

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First meeting of the new CEPEJ Working Group on cyberjustice and artificial intelligence - Council of Europe

The "AI in Oil and Gas Market - Growth, Trends, and Forecast (2020-2025)" report has been added to ResearchAndMarkets.com's offering.

The AI in Oil and Gas market was valued at USD2 billion in 2019 and is expected to reach USD3.81 billion by 2025, at a CAGR of 10.96% over the forecast period 2020-2025. As the cost of IoT sensors declines, more major oil and gas organizations are bound to start integrating these sensors into their upstream, midstream, and downstream operations along with AI-enabled predictive analytics.

Oil and gas remains as one of the most highly valued commodities in the energy sector. In recent years, there has been an increased focus on improving efficiency, and reducing downtime has been a priority for the oil and gas companies as their profits slashed since 2014, due to fluctuating oil prices. However, as concerns over the environmental impact of energy production and consumption persist, oil and gas companies are actively seeking innovative approaches to achieve their business goals, while reducing environmental impact.

In addition, the Oil and Gas Authority (OGA) is making use of AI in parallel ways, owing to the United Kingdom's first oil and gas National Data Repository (NDR), launched in March 2019, using AI to interpret data, which, according to the OGA anticipations, is likely to assist to discover new oil and gas forecast and permit more production from existing infrastructures.

The offshore oil and gas business use AI in data science to make the complex data used for oil and gas exploration and production more reachable, which lets companies to discover new exploration prospects or make more use out of existing infrastructures. For instance, in January 2019, BP invested in Houston-based technology start-up, Belmont Technology, to bolster the company's AI capabilities, developing a cloud-based geoscience platform nicknamed Sandy.

However, high capital investments for the integration of AI technologies, along with the lack of skilled AI professionals, could hinder the growth of the market. A recent poll validated that 56% of senior AI professionals considered that a lack of additional and qualified AI workers was the only biggest hurdle to be overcome, in terms of obtaining the necessary level of AI implementation across business operations.

Key Market Trends

Upstream Operations to Witness a Significant Growth

North America Expected to Hold a Significant Market Share

Competitive Landscape

The AI in the oil and gas market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. The companies are continuously capitalizing on acquisitions, in order to broaden, complement, and enhance its product and service offerings, to add new customers and certified personnel, and to help expand sales channels.

Recent Industry Developments

Key Topics Covered

1 INTRODUCTION

1.1 Study Assumptions and Market Definition

1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

4.1 Market Overview

4.2 Industry Attractiveness - Porter's Five Forces Analysis

4.3 Technology Snapshot - By Application

4.3.1 Quality Control

4.3.2 Production Planning

4.3.3 Predictive Maintenance

4.3.4 Other Applications

5 MARKET DYNAMICS

5.1 Market Drivers

5.1.1 Increasing Focus to Easily Process Big Data

5.1.2 Rising Trend to Reduce Production Cost

5.2 Market Restraints

5.2.1 High Cost of Installation

5.2.2 Lack of Skilled Professionals across the Oil and Gas Industry

6 MARKET SEGMENTATION

6.1 By Operation

6.1.1 Upstream

6.1.2 Midstream

6.1.3 Downstream

6.2 By Service Type

6.2.1 Professional Services

6.2.2 Managed Services

6.3 Geography

6.3.1 North America

6.3.2 Europe

6.3.3 Asia-Pacific

6.3.4 Latin America

6.3.5 Middle East & Africa

7 COMPETITIVE LANDSCAPE

7.1 Company Profiles

7.1.1 Google LLC

7.1.2 IBM Corporation

7.1.3 FuGenX Technologies Pvt. Ltd.

7.1.4 Microsoft Corporation

7.1.5 Intel Corporation

7.1.6 Royal Dutch Shell PLC

7.1.7 PJSC Gazprom Neft

7.1.8 Huawei Technologies Co. Ltd.

7.1.9 NVIDIA Corp.

7.1.10 Infosys Ltd.

7.1.11 Neudax

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET

For more information about this report visit https://www.researchandmarkets.com/r/14dtcc

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Artificial Intelligence in the Oil & Gas Industry, 2020-2025 - Upstream Operations to Witness Significant Growth - ResearchAndMarkets.com - Yahoo...

News item | 21-04-2020 | 04:58

Singapore has joined the EUREKA Clusters Artificial Intelligence (AI) Call. Through this new initiative, Singapore and Dutch companies can receive support in the facilitation of and funding for joint innovation projects in the AI domain with entities from 14 other EUREKA countries. The 14 partner countries are Austria, Belgium, Canada, Denmark, Finland, Germany, Hungary, Luxembourg, Malta, Portugal, Spain, Sweden, South Korea and Turkey. The call will be open from 1 April to 15 June 2020, with funding decisions to be made by January 2021.

The EUREKA Clusters CELTIC-NEXT, EUROGIA, ITEA 3, and PENTA-EURIPIDES, have perceived a common cross domain interest in developing, adapting and utilising emerging Artificial Intelligence within and across their focus areas. These Clusters, together with a number of EUREKA Public Authorities, are now launching a Call for innovative projects in the AI domain. The aim of this Call is to boost the productivity & competitiveness of European industries through the adoption and use of AI systems and services.

The call for proposals is open to projects that apply AI to a large number of application areas, including but not limited to Agriculture, Circular Economy, Climate Response, Cybersecurity, eHealth, Electronic Component and Systems, ICT and applications, Industry 4.0, Low Carbon Energy, Safety, Transport and Smart Mobility, Smart Cities, Software Innovation, and Smart Engineering.

More information: https://eureka-clusters-ai.eu/

To find partners please check the online brokerage tool:https://eureka-clusters-ai.eu/brokerage-tool/

The Netherlands Enterprise Agency (RVO) will host a webinar on Tuesday 28th of April at 10am CEST for Dutch based potential applicants or intermediaries, register here.

Enterprise Singapore will host a webinar on Monday 27 April at 4pm (SG time) for Singapore based potential applicants or intermediaries, register here.

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EUREKA Clusters Artificial Intelligence (AI) Call | News item - The Netherlands and You

LONDON--(BUSINESS WIRE)--The artificial intelligence (AI) market in retail sector is expected to grow by USD 14.05 billion during 2019-2023. The report also provides the market impact and new opportunities created due to the COVID-19 pandemic. The impact can be expected to be significant in the first quarter but gradually lessen in subsequent quarters with a limited impact on the full-year economic growth, according to the latest market research report by Technavio. Request a free sample report

Companies operating in the retail sector are increasingly adopting AI solutions to improve efficiency and productivity of operations through real-time problem-solving. For instance, the integration of AI with inventory management helps retailers to effectively plan their inventories with respect to demand. AI also helps retailers to identify gaps in their online product offerings and deliver a personalized experience to their customers. Many such benefits offered by the integration of AI are crucial in driving the growth of the market.

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR31763

As per Technavio, the increased applications in e-commerce will have a positive impact on the market and contribute to its growth significantly over the forecast period. This research report also analyzes other significant trends and market drivers that will influence market growth over 2019-2023.

Artificial Intelligence (AI) Market in Retail Sector: Increased Applications in E-commerce

E-commerce companies are increasingly integrating AI in various applications to gain a competitive advantage in the market. The adoption of AI-powered tools helps them to analyze the catalog in real-time to serve customers with similar and relevant products. This improves both sales and customer satisfaction. E-commerce companies are also integrating AI with other areas such as planning and procurement, production, supply chain management, in-store operations, and marketing to improve overall efficiency. Therefore, the increasing application areas of AI in e-commerce is expected to boost the growth of the market during the forecast period.

Bridging offline and online experiences and the increased availability of cloud-based applications will further boost market growth during the forecast period, says a senior analyst at Technavio.

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Artificial Intelligence (AI) Market in Retail Sector: Segmentation Analysis

This market research report segments the artificial intelligence (AI) market in retail sector by application (sales and marketing, in-store, planning, procurement, and production, and logistics management) and geographic landscape (North America, APAC, Europe, MEA, and South America).

The North America region led the artificial intelligence (AI) market in retail sector in 2018, followed by APAC, Europe, MEA, and South America respectively. During the forecast period, the North America region is expected to register the highest incremental growth due to factors such as early adoption of AI, rising investments in R&D and start-ups, and increasing investments in technologies.

Technavios sample reports are free of charge and contain multiple sections of the report, such as the market size and forecast, drivers, challenges, trends, and more. Request a free sample report

Some of the key topics covered in the report include:

Market Drivers

Market Challenges

Market Trends

Vendor Landscape

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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Pre & Post COVID-19 Market Estimates-Artificial Intelligence (AI) Market in Retail Sector 2019-2023| Increased Efficiency of Operations to Boost...

DUBLIN, April 23, 2020 /PRNewswire/ -- The "Artificial Intelligence Market by Technology, Infrastructure, Components, Devices, Solutions, and Industry Verticals 2020-2025" report has been added to ResearchAndMarkets.com's offering.

Key Highlights

This report provides a multi-dimensional view into the AI market including analysis of embedded devices and components, embedded software, and AI platforms. This research also assesses the combined Artificial Intelligence (AI) marketplace including embedded IoT and non-IoT devices, embedded components (including AI chipsets), embedded software and AI platforms, and related services.

The report evaluates leading solution providers including hardware, software, integrated platforms, and services. The report includes quantitative analysis with forecasts covering AI technology and systems by type, use case, application, and industry vertical. The forecast also covers each major market sector including consumer, enterprise, industrial, and government. The report also includes specific industry recommendations with respect to Artificial Intelligence hardware, software and services including:

AI Chipsets: The AI chipset marketplace is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. This will also be transformational for existing critical business functions such as Identity management, authentication, and cybersecurity. Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden pattern among data, predict actionable insight, and perform actions based on specific situations. AI chipsets will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance.

AI in Edge Networks: Computing at the edge of IT and communications networks will require a different kind of intelligence. AI will be required for both security (data and infrastructure) as well as to optimize the flow of information in the form of streaming data and the ability to optimize decision-making via real-time data analytics. Edge networks will be the point of the spear so to speak when it comes to data handling, meaning that streaming data will be available for processing and decision-making. While advanced data analytics software solutions can be very effective for this purpose, there will be opportunities to enhance real-time data analytics by way of leveraging AI to automate decision making and to engage machine learning for ongoing efficiency and effectiveness improvements.

AI and 5G: The role and importance of AI in 5G ranges from optimizing resource allocation to data security and protection of network and enterprise assets. However, the concept of using AI in networking is a relatively new area that will ultimately require a more unified approach to fully realize its great potential. In addition, AI will assist 5G network slicing, which represents the ability to dynamically allocate bandwidth, and enforce associated service level agreements, and a per-customer and per-application basis. AI will automate the process of assigning network slices, including informing enterprise customers when the slices they are requesting are not in their best interest based on anticipated network conditions.

AI and Real-time Data Processing: The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In addition, AI will support data management across all of these areas. The growing amount of human-oriented and machine-generated from communications, applications, content, and commerce data will drive substantial opportunities for AI support of unstructured data analytics solutions.

The outlook for AI in support of the ICT industry is strong, especially when one considers that the purpose of telecom and IT services is to support virtually every other industry in terms of communications, applications, content, and commerce.

Key Topics Covered

1. Executive Summary

2. Overview2.1 Defining Artificial Intelligence2.2 Artificial General Intelligence2.3 Artificial Super Intelligence2.4 Artificial Intelligence Types2.5 Artificial Intelligence Language2.6 Artificial Intelligence Systems2.7 AI Outcomes and Enterprise Benefits2.8 Conversational User Interfaces2.9 Cognitive Computing and Swarm Intelligence2.10 AI Market Drivers and Impact2.11 AI Market Constraints2.12 AI Market Opportunities2.13 AI Market Outlook and Predictions

3. Technology Impact Analysis3.1 AI Technology Matrix3.1.1 Machine Learning3.1.1.1 Deep Learning3.1.1.2 Supervised vs. Unsupervised Learning3.1.1.3 Reinforcement Learning3.1.2 Natural Language Processing3.1.3 Computer Vision3.1.4 Speech Recognition3.1.5 Context-Aware Processing3.1.6 Artificial Neural Network3.1.7 Predictive APIs3.1.8 Autonomous Robotics3.2 AI Technology Readiness3.3 Machine Learning APIs3.3.1 IBM Watson API3.3.2 Microsoft Azure Machine Learning API3.3.3 Google Prediction API3.3.4 Amazon Machine Learning API3.3.5 BigML3.3.6 AT&T Speech API3.3.7 Wit.ai3.3.8 AlchemyAPI3.3.9 Diffbot3.3.10 PredictionIO3.3.11 General Application Environment3.4 AI Technology Goal3.5 AI Tools and Approaches3.6 Emotion AI3.6.1 Facial Detection APIs3.6.2 Text Recognition APIs3.6.3 Speech Recognition APIs3.7 IoT Application and Big Data Analytics3.8 Data Science and Predictive Analytics3.9 Edge Computing and 5G Network3.10 Cloud Computing and Machine Learning3.11 Smart Machine and Virtual Twinning3.12 Factory Automation and Industry 4.03.13 Building Automation and Smart Workplace3.14 Cloud Robotics and Public Security3.15 Self-Driven Network and Domain-Specific Network3.16 Predictive 3D Design

4. Market Solutions and Applications Analysis4.1 AI Market Landscape4.1.1 Embedded Device and Things4.1.2 AI Software and Platform4.1.3 AI Component and Chipsets4.1.4 AI Service and Deployment4.2 AI Application Delivery Platform4.3 AIaaS and MLaaS4.4 Enterprise Adoption and External Investment4.5 Enterprise AI Drive Productivity Gains4.6 AI Patent and Regulatory Framework4.7 Value Chain Analysis4.7.1 Artificial Intelligence Companies4.7.2 IoT Companies and Suppliers4.7.3 Data Analytics Providers4.7.4 Connectivity Infrastructure Providers4.7.5 Components and Chipsets Manufacturers4.7.6 Software Developers and Data Scientists4.7.7 End Users4.7.8 End-User Industry and Application4.8 AI Use Case Analysis4.9 Competitive Landscape Analysis

5. Company Analysis5.1 NVIDIA Corporation5.2 IBM Corporation5.3 Intel Corporation5.4 Samsung Electronics Co Ltd.5.5 Microsoft Corporation5.6 Google Inc.5.7 Baidu Inc.5.8 Qualcomm Incorporated5.9 Huawei Technologies Co. Ltd.5.10 Fujitsu Ltd.5.11 H2O.ai5.12 Juniper Networks, Inc.5.13 Nokia Corporation5.14 ARM Limited5.15 Hewlett Packard Enterprise (HPE)5.16 Oracle Corporation5.17 SAP5.18 Siemens AG5.19 Apple Inc.5.20 General Electric (GE)5.21 ABB Ltd.5.22 LG Electronics5.23 Koninklijke Philips N.V5.24 Whirlpool Corporation5.25 AB Electrolux5.26 Wind River Systems Inc.5.27 Cumulocity GmBH5.28 Digital Reasoning Systems Inc.5.29 SparkCognition Inc.5.30 KUKA AG5.31 Rethink Robotics5.32 Motion Controls Robotics Inc.5.33 Panasonic Corporation5.34 Haier Group Corporation5.35 Miele5.36 Next IT Corporation5.37 Nuance Communications Inc.5.38 InteliWISE5.39 Facebook Inc.5.40 Salesforce5.41 Amazon Inc.5.42 SK Telecom5.43 motion.ai5.44 Buddy5.45 AOL Inc.5.46 Tesla Inc.5.47 Inbenta Technologies Inc.5.48 Cisco Systems5.49 MAANA5.50 Veros Systems Inc.5.51 PointGrab Ltd.5.52 Tellmeplus5.53 Xiaomi Technology Co. Ltd.5.54 Leap Motion Inc.5.55 Atmel Corporation5.56 Texas Instruments Inc.5.57 Advanced Micro Devices (AMD) Inc.5.58 XILINX Inc.5.59 Omron Adept Technology5.60 Gemalto N.V.5.61 Micron Technology5.62 SAS Institute Inc.5.63 AIBrian Inc.5.64 QlikTech International AB5.65 MicroStrategy Incorporated5.66 Brighterion Inc.5.67 IPsoft Inc.5.68 24/7.ai Inc.5.69 General Vision Inc.5.70 Sentient Technologies Holdings Limited5.71 Graphcore5.72 CloudMinds5.73 Rockwell Automation Inc.5.74 Tend.ai5.75 SoftBank Robotics Holding Corp.5.76 iRobot Corp.5.77 Lockheed Martin5.78 Spacex5.79 Fraight AI5.80 Infor Global Solutions5.81 Presenso5.82 Teknowlogi

6. AI Market Analysis and Forecasts 2019-20246.1 AI Market6.2 AI Market by Segment6.2.1 Hardware6.2.1.1 Embedded Device6.2.1.1.1 Non-IoT Device6.2.1.1.2 IoT Device6.2.1.1.2.1 Wearable Devices6.2.1.1.2.2 Medical and Healthcare Devices6.2.1.1.2.3 Smart Appliances6.2.1.1.2.4 Industrial Machines6.2.1.1.2.5 Robots and Drone6.2.1.1.2.6 Service Robots6.2.1.1.2.7 Entertainment Devices6.2.1.1.2.8 Security Devices6.2.1.1.2.9 Networking Device6.2.1.1.2.10 In-Vehicle IoT Device6.2.1.1.2.11 Smart Grid Device6.2.1.1.2.12 Military Device6.2.1.1.2.13 Energy Management Device6.2.1.1.2.14 Agriculture Specific Device6.2.1.2 Embedded IoT System6.2.1.3 Semiconductor Components6.2.1.3.1 Wearable and Embedded Components6.2.1.3.1.1 Real-Time Location System (RTLS)6.2.1.3.1.2 Barcode6.2.1.3.1.3 Barcode Scanner6.2.1.3.1.4 Barcode Scanner Technology Levels6.2.1.3.1.5 Barcode Stickers6.2.1.3.1.6 RFID6.2.1.3.1.7 RFID Tags6.2.1.3.1.8 Sensor6.2.1.3.2 Processors6.2.2 Software6.2.2.1 Software Category6.2.2.1.1 AI Platform6.2.3 Services6.2.3.1 Professional Services6.3 AI Market by Management Functions6.4 AI Market by Technology6.4.1 Machine Learning6.5 AI Market by Industry Vertical6.5.1 Medical and Healthcare6.5.2 Manufacturing6.5.3 Consumer Electronics6.5.4 Automotive and Transportation6.5.5 Retail and Apparel6.5.6 Marketing and Advertising6.5.7 FinTech6.5.8 Building and Construction6.5.9 Agriculture6.5.10 Security and Surveillance6.5.11 Government, Military, and Aerospace6.5.12 Human Resource6.5.13 Legal and Law6.5.14 Telecommunication and IT6.5.15 Oil, Gas, and Mining6.5.16 Logistics6.5.17 Education and Learning6.6 AI Market by Solution6.7 AI Market by Deployment6.7.1 Cloud Deployment6.8 AI Market by AI System6.9 AI Market by AI Type6.10 AI Market by Connectivity6.10.1 Non-Telecom Connectivity6.10.2 Telecom Connectivity6.10.3 Connectivity Standard6.10.4 Enterprise6.11 AI Market by IoT Network6.12 AI Market by IoT Edge Network6.13 AI Analytics Market6.14 AI Market by Intent-Based Networking6.15 AI Market by Virtualization6.16 AI Market by 5G Network6.17 AI Market by Blockchain Network6.18 AI Market by Region6.18.1 North America6.18.2 Asia Pacific6.18.2.1 China6.18.2.2 South Korea6.18.2.3 Taiwan6.18.2.4 Rest of Asia6.18.3 Europe6.18.4 Middle East and Africa6.18.5 Latin America6.19 AI Embedded Unit Deployment Forecast6.19.1 Unit Deployment by Solution6.19.1.1 Non-IoT Device6.19.1.2 IoT Device6.19.1.3 IoT Things and Objects6.19.1.4 IoT Semiconductor6.19.1.5 Software6.19.2 Unit Deployment by Region6.19.2.1 North America6.19.2.2 Asia-Pacific6.19.2.3 Europe6.19.2.4 Middle East and Africa6.19.2.5 Latin America

7. Conclusions and Recommendations

For more information about this report visit https://www.researchandmarkets.com/r/99dx1y

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Global Artificial Intelligence (AI) Industry Outlook, 2020-2025 - AI Chipsets, AI in Edge Networks, AI and 5G, AI and Real-time Data Processing -...

Banking has the potential to improve its customer service, loan applications, and billing with the help of AI and natural language processing.

Image: Kubkoo, Getty Images/iStockPhoto

When I was an executive in banking, we struggled with how to transform tellers at our branches into customer service specialists instead of the "order takers" that they were. This struggle with customer service is ongoing for financial institutions. But it's an area in which artificial intelligence (AI), and its ability to work with unstructured data like voice and images, can help.

"There are two things that artificial intelligence does really well," said Ameek Singh, vice president of IBM's Watson applications and solutions. "It's really good with analyzing images and it also performs uniquely well with natural language processing (NLP)."

SEE:Managing AI and ML in the enterprise 2020 (free PDF)(TechRepublic)

AI's ability to process natural language helps behind the scenes as banks interact with their customers. In call center banking transactions, the ability to analyze language can detect emotional nuances from the speaker, and understand linguistic differences such as the difference between American and British English. AI works with other languages as well, understanding the emotional nuances and slang terms that different groups use.

Collectively, real-time feedback from AI aids bank customer service reps in call centersbecause if they know the sentiments of their customers, it's easier for them to relate to customers and to understand customer concerns that might not have been expressed directly.

"We've developed AI models for natural language processing in a multitude of languages, and the AI continues to learn and refine these linguistics models with the help of machine learning (ML)," Singh said.

SEE:AI isn't perfect--but you can get it pretty darn close(TechRepublic)

The result is higher quality NLP that enables better relationships between customers and the call center front line employees who are trying to help them.

But the use of AI in banking doesn't stop there. Singh explained how AI engines like Watson were also helping on the loans and billing side.

"The (mortgage) loan underwriter looks at items like pay stubs and credit card statements. He or she might even make a billing inquiry," Singh said.

Without AI, these document reviews are time consuming and manual. AI changes that because the AI can "read" the document. It understands what the salient information is and also where irrelevant items, like a company logo, are likely to be located. The AI extracts the relevant information, places the information into a loan evaluation model, and can make a loan recommendation that the underwriter reviews, with the underwriter making a final decision.

Of course, banks have had software for years that has performed loan evaluations. However, they haven't had an easy way to process foundational documents such as bills and pay stubs, that go into the loan decisioning process and that AI can now provide.

SEE:These five tech trends will dominate 2020(ZDNet)

The best news of all for financial institutions is that AI modeling and execution don't exclude them.

"The AI is designed to be informed by bank subject matter experts so it can 'learn' the business rules that the bank wants to apply," Singh said. "The benefit is that real subject matter experts get involvednot just the data scientists."

Singh advises banks looking at expanding their use of AI to carefully select their business use cases, without trying to do too much at once.

"Start small instead of using a 'big bang' approach," he said. "In this way, you can continue to refine your AI model and gain success with it that immediately benefits the business."

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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Artificial intelligence can take banks to the next level - TechRepublic

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues.

Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. Thats equivalent to nearly five times the lifetime emissions of the average U.S. car, including its manufacturing.

This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources.

MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved in some cases, down to low triple digits.

The researchers system, which they call a once-for-all network, trains one large neural network comprising many pretrained subnetworks of different sizes that can be tailored to diverse hardware platforms without retraining. This dramatically reduces the energy usually required to train each specialized neural network for new platforms which can include billions of internet of things (IoT) devices. Using the system to train a computer-vision model, they estimated that the process required roughly 1/1,300 the carbon emissions compared to todays state-of-the-art neural architecture search approaches, while reducing the inference time by 1.5-2.6 times.

The aim is smaller, greener neural networks, says Song Han, an assistant professor in the Department of Electrical Engineering and Computer Science. Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.

The work was carried out on Satori, an efficient computing cluster donated to MIT by IBM that is capable of performing 2 quadrillion calculations per second. The paper is being presented next week at the International Conference on Learning Representations. Joining Han on the paper are four undergraduate and graduate students from EECS, MIT-IBM Watson AI Lab, and Shanghai Jiao Tong University.

Creating a once-for-all network

The researchers built the system on a recent AI advance called AutoML (for automatic machine learning), which eliminates manual network design. Neural networks automatically search massive design spaces for network architectures tailored, for instance, to specific hardware platforms. But theres still a training efficiency issue: Each model has to be selected then trained from scratch for its platform architecture.

How do we train all those networks efficiently for such a broad spectrum of devices from a $10 IoT device to a $600 smartphone? Given the diversity of IoT devices, the computation cost of neural architecture search will explode, Han says.

The researchers invented an AutoML system that trains only a single, large once-for-all (OFA) network that serves as a mother network, nesting an extremely high number of subnetworks that are sparsely activated from the mother network. OFA shares all its learned weights with all subnetworks meaning they come essentially pretrained. Thus, each subnetwork can operate independently at inference time without retraining.

The team trained an OFA convolutional neural network (CNN) commonly used for image-processing tasks with versatile architectural configurations, including different numbers of layers and neurons, diverse filter sizes, and diverse input image resolutions. Given a specific platform, the system uses the OFA as the search space to find the best subnetwork based on the accuracy and latency tradeoffs that correlate to the platforms power and speed limits. For an IoT device, for instance, the system will find a smaller subnetwork. For smartphones, it will select larger subnetworks, but with different structures depending on individual battery lifetimes and computation resources. OFA decouples model training and architecture search, and spreads the one-time training cost across many inference hardware platforms and resource constraints.

This relies on a progressive shrinking algorithm that efficiently trains the OFA network to support all of the subnetworks simultaneously. It starts with training the full network with the maximum size, then progressively shrinks the sizes of the network to include smaller subnetworks. Smaller subnetworks are trained with the help of large subnetworks to grow together. In the end, all of the subnetworks with different sizes are supported, allowing fast specialization based on the platforms power and speed limits. It supports many hardware devices with zero training cost when adding a new device.In total, one OFA, the researchers found, can comprise more than 10 quintillion thats a 1 followed by 19 zeroes architectural settings, covering probably all platforms ever needed. But training the OFA and searching it ends up being far more efficient than spending hours training each neural network per platform. Moreover, OFA does not compromise accuracy or inference efficiency. Instead, it provides state-of-the-art ImageNet accuracy on mobile devices. And, compared with state-of-the-art industry-leading CNN models , the researchers say OFA provides 1.5-2.6 times speedup, with superior accuracy. Thats a breakthrough technology, Han says. If we want to run powerful AI on consumer devices, we have to figure out how to shrink AI down to size.

The model is really compact. I am very excited to see OFA can keep pushing the boundary of efficient deep learning on edge devices, says Chuang Gan, a researcher at the MIT-IBM Watson AI Lab and co-author of the paper.

If rapid progress in AI is to continue, we need to reduce its environmental impact, says John Cohn, an IBM fellow and member of the MIT-IBM Watson AI Lab. The upside of developing methods to make AI models smaller and more efficient is that the models may also perform better.

Original post:
Reducing the carbon footprint of artificial intelligence - MIT News