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Federal Government Moving to Implement AI More Widely

As the federal government moves to a wider implementation of AI, we highlight selected implementations at the VA and the IRS. (GETTY IMAGES)
By AI Trends Staff
The VA is planning to expand its use of AI; the IRS is moving to employ more AI for help with tax compliance. …

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Retailers Pursuing AI to Help Negotiate Prices, Enhance Operations

Retailers are expanding their use of AI beyond online shopping to contract negotiations with suppliers and achieving more efficient operations. (GETTY IMAGES)
By AI Trends Staff
Retailers are not only using AI to enhance the online shopping experience. Walmart has an agreement to pilot AI technology from Pactum to help …

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Executive Interview: Beena Ammanath Boosts Women and Diversity in Tech as AI Expands

Humans for AI was founded by Beena Ammanath to increase opportunities for women and to diversify the tech workforce as AI continues its march through business. (HUMANS FOR AI)
Putting Guardrails in Place for Women and Diversity in Tech as AI Is Infused Into Everything in the Organization
Beena Ammanath …

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Does Your Business Really Need an AI Solution?

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

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AI is Changing the Pattern for How Software is Developed

AI is helping companies to deploy new software more efficiently, and to allow a new generation of developers to learn to code more easily. Credit: Getty Images 

By AI Trends Staff  

Software developers are using AI to help write and review code, detect bugs, test software and optimize development projects. This assistance is helping companies to deploy new software more efficiently, and to allow a new generation of developers to learn to code more easily. 

These are conclusions of a recent report on AI in software development published by Deloitte and summarized in a recent article in Forbes. Authors David Schatsky and Sourabh Bumb describe how a range of companies have launched dozens of AI-driven software development tools over the past 18 months. The market is growing with startups raising $704 million in the year ending September 2019.  

The new tools can be used to help reduce keystrokes, detect bugs as software is being written and automate many of the tests needed to confirm the quality of software. This is important in an era of increasing reliance on open source code, which can come with bugs. 

While some fear automation may take jobs away from coders, the Deloitte authors see it as unlikely.  

“For the most part, these AI tools are helping and augmenting humans, not replacing them,” Schatsky stated. “These tools are helping to democratize coding and software development, allowing individuals not necessarily trained in coding to fill talent gaps and learn new skills. There is also AI-driven code review, providing quality assurance before you even run the code.” 

A study from Forrester in 2018 found that 37 percent of companies involved in software development were using coding tools powered by AI. The percentage is likely to be higher now, with companies such as Tara, DeepCode, Kite, Functionize and Deep TabNine and many others providing automated coding services. 

Success seems to be accelerating the trend. “Many companies that have implemented these AI tools have seen improved quality in the end products, in addition to reducing both cost and time,” stated Schatsky.  

The Deloitte study said AI can help alleviate a chronic shortage of talented developers. Poor software quality cost US organizations an estimated $319 billion last year. The application of AI has the potential to mitigate these challenges. 

Deloitte sees AI helping in many stages of software development, including: project requirements, coding review, bug detection and resolution, more through testing, deployment and project management.     

IBM Engineer Learned AI Development Lessons from Watson Project 

IBM Distinguished Engineer Bill Higgins, based in Raleigh, NC, who has spent 20 years in software development at the company, recently published an account on the impact of AI in software development in Medium.  

Organizations need to “unlearn” the patterns for how they have developed software in the past. “If it’s difficult for an individual to adapt, it’s a million times harder for a company to adapt,” the author stated.   

Higgins was the lead for IBM’s AI for developers mission within the Watson group. “It turned out my lack of personal experience with AI was an asset,” he stated. He had to go through his own learning journey and thus gained deeper understanding and empathy for developers needing to adapt.  

To learn about AI in software development, Higgins said he studied how others have applied it (the problem space) and the cases in which using AI is superior to alternatives (the solution space). This was important to understanding what was possible and to avoid “magical thinking.” 

The author said his journey was the most intense and difficult learning he had done since getting a computer science degree at Penn State. “It was so difficult to rewire my mind to think about software systems that improve from experience, vs. software systems that merely do the things you told them to do,” he stated.  

IBM developed a conceptual model to help enterprises think about AI-based transformation called the AI Ladder. The ladder has four rungs: collect, organize, analyze and infuse. Most enterprises have lots of data, often organized in siloed IT work or from acquisitions. A given enterprise may have 20 databases and three data warehouses with redundant and inconsistent information about customers. The same is true for other data types such as orders, employees and product information. “IBM promoted the AI Ladder to conceptually climb out of this morass,” Higgins stated.  

In the infusion stage, the company works to integrate trained machine learning models into production systems, and design feedback loops so the models can continue to improve from experience. An example of infused AI is the Netflix recommendation system, powered by sophisticated machine learning models. 

IBM had determined that a combination of APIs, pre-built ML models and optional tooling to encapsulate, collect, organize and analyze rungs of the AI ladder for common ML domains such as natural language understanding, conversations with virtual agents, visual recognition, speech and enterprise search. 

For example, Watson’s Natural Language Understanding became rich and complex. Machine learning is now good at understanding many aspects of language including concepts, relationships between concepts and emotional content. Now the NLU service and the R&D on machine learning-based natural language processing can be made available to developers via an elegant API and supporting SDKs. 

Thus developers can today begin leveraging certain types of AI in their applications, even if they lack any formal training in data science or machine learning,” Higgins stated.  

It does not eliminate the AI learning curve, but it makes it a more gentle curve.  

Read the source articles in Forbes and  Medium.  

Source: AI Trends

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Companies Bringing AI Training Inside; Udacity Well-Positioned  

AI is having an impact on the training market, estimated to be at $169 billion annually in North America; $83 billion of that is workplace training. Credit: Getty Images

By John P. Desmond, AI Trends Editor  

Companies are taking AI training into their own hands, hiring outside firms to help their employees to learn about AI and often picking up the expense.   

Training is a big market. The annual North American workplace training market is estimated to be $169 billion, according to an estimate on Statista. Spending on annual workplace training averaged $83 billion from 2012 to 2019. The share of US companies that partially or fully outsource training is 53%.   

Royal Dutch Shell could be a model. The company is expanding an online program that teaches AI skills as part of an effort to cut costs, improve business processes, and generate revenue, according to a recent account in WSJ Pro. Of its 82,000 employees, about 2,000 have expressed interest or been approached by management about taking AI courses through Udacity, the online education company. These include petroleum engineers, chemists, and geophysicists.  

The courses are voluntary and employees can complete them at their own pace during work hours; the company covers the cost of the training.  

Yuri Sebregts, Shell’s chief technology officer

“Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights, which can keep us ahead of the competition,” said Yuri Sebregts, Shell’s chief technology officer, stated in an email to the Wall Street Journal. 

In a pilot program with Udacity in 2019, Shell trained 250 data scientists and software engineers in areas including reinforcement learning, a branch of machine learning where algorithms improve by trial and error. The scientists are applying the models to better predict machine failures, reduce carbon emissions and process seismic and geological rock formation data. This could assist in decisions about where to drill.  

“Technology is moving so quickly that if you’re not continually training your people, you’re going to get out of date,” stated Dan Jeavons, Shell’s general manager of data science.  

 The trend of companies taking AI training inside is a boon for Udacity. Courses normally cost $400/month for individuals. Udacity Chief Executive Babe Dalporto was quoted as saying that the firm’s courses for corporate employees are likely to be its largest business this year, with interest accelerating over the past two years.  

“Any Fortune 500 company is realizing that AI is going to be disruptive,” stated Sebastian Thrun, Udacity’s founder, president and executive chairman.   

IBM Launches AI Skills Academy 

Obed Louissant, VP Talent, Watson Health and Employee Experience, IBM

In December 2018, IBM launched its AI Skills Academy (AISA) with two objectives, according to a recent account in Fortune. First, it was to teach employees how to integrate AI into their own jobs in the company, ranging from marketing to supply chain responsibilities. Second, AISA teaches how to collaborate with clients to use AI in their businesses. The program has two tracks: technical and non-technical, with four levels in each track, basic to expert.   

Some 2,200 IBMers had started the training in its first six months or so; IBM expected 4,000 to complete all four levels by the end of 2019. “That’s just for openers,” stated Obed Louissant, VP Talent, Watson Health and Employee Experience, IBM. “All of our employees will eventually be trained in AI.” New courses are continually added, for instance in general management.  

While it might have been risky for IBM to commit to training its own workforce in AI, for fear of making them more attractive to other employers, attrition from the trained group has been lower than average so far. In surveys on what motivates them, training came up big. “They are most interested in keeping up with the cutting edge in technology and continually learning new skills,” stated Louissant. “So offering them new training is a retention strategy.”  

Learning and Development Professionals Tuning into AI 

Learning and development professionals need to stay on top of the impact of AI on their market, suggests a recent article posted on the blog of Virtual Speech, written by company co-founder, Dom Barnard. For example, teaching styles need to be personalized and customized for males and females. AI can be employed to help focus on weaker areas of the learner, recommend suitable content, predict needs based on the learner’s role, and auto-generate content using content creation algorithms. 

Learning preferences can range from video tutorials, written content, in-person training, gamification, and audio-guided presentations. An AI-powered learning management system can adapt, offering video tutorials to certain employees, and auto-transcribing the videos to text-based articles for other employees. The system could create visuals based on written content, and suggest the employee take an in-person training day if they struggle with a certain section. 

Online assessments can also adapt, tailored to the individual’s ability and progression. An example is Iris, developed by PluralSight, a technical training provider. Iris adjusts question difficulty and skill ratings as the learner progresses. Using natural language processing and machine learning, it recommends assessment content. 

Virtual Speech combines employees training with virtual reality to give users a realistic way to practice different soft skills and provide instant feedback on performance. The company offers a white paper on VR and AI for Soft Skills Training. 

Savvy employers can use AI to help “upskill” employees, to be proactive in ensuring the most relevant training tools and knowledge content is available to employees when they want to learn. Private industry is of course responding. An example is startup Guider of the UK, which uses AI to match employees with mentors and to manage the mentoring process.  

AI Can Help Integrate Training into the Routine Workflow 

AI can help integrate workplace training into the regular workflow, suggests a recent entry on the blog of WhatFix, a software company whose platform helps users create interactive walkthroughs for learning to use Web applications and other software. 

An AI-enhanced learning management system can personalize training paths for each individual; improve completion rates; build content at scale; frees up learning and development staff for more in-person training sessions; remove training bias; and measure effectiveness. 

The system can also venture into new learning models such as game-based courses and storyline-based games. The behaviors are tracked to customize learning to make it more interesting for employees.  

Read the source articles at Statista, in WSJ ProFortune, the blog of Virtual Speech, and the blog of  WhatFix. 





Source: AI Trends

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GSA Unit Launches AI Community of Practice to Boost Agency Adoption

A GSA unit has formed an AI community of practice that aims to accelerate AI adoption across the federal government by compiling helpful use cases. (GETTY IMAGES)

By AI Trends Staff

The General Services Administration’s Technology Transformation Services (TTS) unit has launched an AI community of practice (AI CoP) to capture advances in AI and accelerate adoption across the federal government. The founding was announced in November via a blog post written by Steve Babitch, head of the AI portfolio for TTS.

The action is a follow-up to an Executive Order signed by President Trump in February on Maintaining American Leadership in AI. “The initiative implements a government-wide strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners,” Babitch stated in the blog post.

Steve Babitch, head of the AI portfolio for TTS unit of the GSA

He outlined these six areas where the AI CoP will support and coordinate the use of AI technologies in federal agencies:

  • Machine Learning and deep learning
  • Robotic Process Automation
  • Human-computer interactions
  • Natural Language Processing
  • Rule based automation
  • Robotics

The executive sponsors of the AI CoP are the Federal Chief Information Officer, Suzette Kent, and the Director of GSA’s Technology Transformation Services, Anil Cheriyan. The CoP will be administered out of the Technology Transformation Services (TTS) Solutions division, led by Babitch, who coordinates with the CIO Council’s Innovation Committee.

Library of AI Use Cases in Government Being Compiled

At a GSA event in January, Babitch described an effort to develop a library of AI use cases that agencies can reference as they start to invest in new AI technology, according to an account in  fedscoop. The library could lead to other practice areas being added to the list.

“The harder we start to build that repository of use cases and build in a searchable database, if you will, that can sort of blossom into other facets as well—different themes or aspects of use cases,” Babitch stated. “Maybe there’s actually a component around culture and mindset change or people development.”

Practice areas mentioned include acquisition, ethics, governance, tools and techniques, and workforce readiness. Early use cases across agencies have touched on customer experience, human resources, advanced cybersecurity, and business processes.

In an example, the Economic Indications Division (EID) of the Census Bureau developed a machine learning model for automating data coding.

“It’s the perfect machine learning project,” stated Rebecca Hutchinson, big data leader at EID. “If you can automate that coding, you can speed up and code more of the data. And if you can code more of the data, we can improve our data quality and increase the number of data products we’re putting out for our data users.”

She reported that the model is performing with about 80% accuracy, leaving only 20% still needing to be manually coded.

One-Third of Census Bureau Staff Enrolled in AI Training

The Census Bureau has been offering AI training to interested workers, many of whom are taking advantage of the opportunity.

Interested staff can apply to learn Python in ArcGIS, and Tableau through a Coursera course. Hutchinson reported that one-third of the bureau’s staff has completed training or is currently enrolled, coming away with ML and web scraping skills.

“Once you start training your staff with the skills, they are coming up with solutions,” Hutchinson stated. “It was our staff that came up with the idea to do machine learning of construction data, and we’re just seeing that more and more.”

Read the source articles on the  GSABlog and in fedscoop.

Source: AI Trends

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AI in the Lead of US Emerging Jobs in 2020, says LinkedIn

The top emerging US job for 2020 is AI specialist, according to the third annual Emerging Jobs report from LinkedIn. (GETTY IMAGES)

By AI Trends Staff

Major trends shown in the LinkedIn’s third annual Emerging Jobs Report recently released by LinkedIn, the professional network company, include show a strong showing for AI, that professionals are moving to the most attractive regions, and demand for soft skills around communication, creativity and collaboration are increasing as automation becomes more widespread.

In top US job trends, data science is booming and starting to replace legacy roles. “Data science is seeing continued growth on a tremendous scale,” the report states. It also shows data scientists are taking on responsibilities that had been in the domain of statisticians.

Engineering roles are seeing tremendous growth; more than 50% of emerging jobs on this year’s list are made of up roles in engineering or development, with robotics engineering jobs appearing on the LinkedIn list for the first time.

Online learning is here to stay, with the multibillion-dollar e-learning industry staffing up to prepare for growth.

Leading the LinkedIn list is artificial intelligence specialist, or someone who focuses on machine learning and ways to incorporate AI technology into business environments, as summarized in a report in the Boston Herald. AI specialist jobs have grown annually by 74% over the last five years as demand in computer software, internet, information technology and consumer electronics industries has increased. AI specialist salaries average $136,000 a year.

Top US regions for AI specialist jobs include San Francisco, New York, Boston, Seattle, and Los Angeles.

Second place went to robotics engineer, with a 40% growth rate and average annual salary of $85,000, while data scientist, and its growth rate of 37% and $143,000 average salary came in third. Fourth place went to full stack engineer, a job that pays $82,000 a year on average and has a 35% growth rate.

Site reliability engineer ($130,000 a year), customer success specialist ($90,000 annually) and sales development representative ($60,000 a year, on average) all tied for fifth place with an annual growth rate of 33%.

The rest of the top 15 emerging jobs included data engineer (33% annual growth rate, $100,000 a year salary), behavioral health technician (growth rate of 32% and an average salary of $33,000 a year), cybersecurity specialist ($103,000 a year) and back end developer ($88,000 a year) both with a 30% growth rate, chief revenue officer (28% growth rate and a $330,000 average salary), cloud engineer (growth rate of 27%, $100,000 in average salary), Javascript developer (growth rate of 25%, $83,000 annual salary) and product owner (24% growth rate with a $100,000 annual salary).

People Skills in Demand

Other big trends include: increasing demand for jobs requiring strong people skills. The new roles include product owner, customer success specialist and sales development representative. For Software as a Service (SaaS), a $278 billion industry, to be successful requires people skills not yet automated.

Also, the competition in self-driving cars has the automotive industry searching for AI talent among robotics engineers, data scientists and AI specialists.

Edge computing is expected to attract more development resources, with a shift occurring to cloud-edge hybrid strategies to overcome limitations of a cloud-only architecture. “Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations,” says Senthil Kumar, VP of software engineering for FogHorn, quoted in a piece in The Enterprisers Project. Many organizations to date have settled for a smaller sample size or time-deferred data for their edge projects, which can provide an incomplete or inaccurate picture.

Guy Berger, Principal Economist, LinkedIn

AI is having an impact on the entire workforce, the LinkedIn report suggests. “Artificial intelligence will require the entire workforce to learn new skills, whether it’s to keep up to date with an existing role, or pursuing a new career as a result of automation,” stated LinkedIn’s Principal Economist Guy Berger.

Read the source report from LInkedIn at Emerging Jobs Report,  summary at the Boston Herald, and an account in The Enterprisers Project.

Source: AI Trends

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

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

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

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

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

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

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

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

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

See the full survey results.

Source: AI Trends