AI and the Org Chart: As Business Deploys AI, “Work Architecture” Need a Redesign

As editor of AI Trends, I am researching the impact of AI on how companies are organized to do work. I am interested in new job descriptions around data science, big data, machine learning, digital knowledge, AI interaction, natural language processing and others not mentioned here but that you might be involved in. I would […]

As editor of AI Trends, I am researching the impact of AI on how companies are organized to do work. I am interested in new job descriptions around data science, big data, machine learning, digital knowledge, AI interaction, natural language processing and others not mentioned here but that you might be involved in. I would like to describe your experience, what brought it about, what your organization is trying to achieve with AI. Please email me. I will respond and start the discussion, respecting all requirements of your organization for outside communication, and always with your permission for what gets published. My hope is to create a guide for AI and business professionals navigating this new and evolving field.]

Some 65% of children entering primary school today will have jobs that do not now exist, according to one estimate. To gain an understanding of what jobs are up and coming, and what skills are needed to succeed, LinkedIn studied data from five years to spot trends.

Among the key findings:

  • Machine learning engineers, data scientists and big data engineers were among the top emerging jobs, with companies in a wide range of industries seeking those skills.
  • Talent is scarce. Data Scientist roles have increases 650 percent since 2012, but currently in the US, 35,000 people are said to have data science skills. The supply of candidates for these roles cannot keep up with demand from the companies hiring.
  • Many of the emerging needed skills did not exist five years ago; many professionals are not confident that their current skill set will still be relevant in one to two years.
  • Software engineers are feeding into all the technology-related professions.

Here are some example strong growth titles from the LinkedIn study:

Machine Learning Engineer

  1. Software Engineer
  2. Research Assistant
  3. Teaching Assistant
  4. Data Scientist
  5. System Engineer

Data Scientist

  1. Research Assistant
  2. Teaching Assistant
  3. Software Engineer
  4. Data Scientist
  5. Business Analyst

Big Data Developer

  1. Software Engineer
  2. Hadoop Developer
  3. System Engineer
  4. Java Engineer
  5. ETL Developer

AI is Seen Adding More Jobs Than Lost

The emergence of AI in the organization was seen to be adding more jobs than those lost by attendees at the EmTech Digital Conference from Ernst & Young and MIT Technology Review held in the spring of 2018.

While many companies are striving to implement AI on projects, few have tied AI into the overall business strategy.  A basic notion is that AI will free people to do more interesting work.

Jeff Wong, EY Global Chief Innovation Officer, said in an article in MIT Technology Review,  “As businesses deploy AI strategies,  they’re increasingly aware of how the roles, responsibilities and skills of their talent is changing.  With AI taking a leading role on tackling organizations’ simple and repetitive tasks, the human workforce can focus more on complex work that ultimately provides a greater level of professional fulfilment to employees and a more efficient use of critical thinking power.”

Asked if AI is being used currently in their organizations, most respondents said AI is being piloted in one or more areas but there was no overall enterprise AI strategy. The next cluster reported that AI is currently not a strategic priority.

Chris Mazzei, EY Chief Data & Analytics Officer and Global Innovation Technologies Leader, stated, “While we’re seeing momentum in businesses deploying AI more strategically across the enterprise, its application is often fragmented across business functions, leaving much of the potential untapped.”

When asked for the top three desired business outcomes from the application of AI, the answers were: to improve and/or develop new products and services; achieve cost efficiencies and streamlined business operations, and to accelerate decision-making.

Chris Mazzei added: “AI technologies have been proven to streamline operations and speed-up internal processes. However, businesses should think more holistically about the competitive advantages that can be reaped from thoughtful applications of AI in product and service development, sales enablement, enhancing customer experience, or capturing business intelligence that helps impact the bottom line.”

The talent shortage is holding things back. “Despite AI’s potential to drive transformational change, adoption continues to be hampered by a shortage of talent,” stated Nigel Duffy, EY Global Innovation Artificial Intelligence Leader. “Businesses must invest in and create a culture of continuous learning that comprises skills programs, training sessions, and research partnerships to attract and retain leading AI practitioners.”

Businesses are aware they need to diversify their AI talent pools to try to prevent bias in results.

Jeff Wong stated, “There is a correlation between the continued lack of diverse AI talent and the distortions being found in some machine-learning outcomes. To mitigate this, businesses need to look for a wide variety of talent to ensure a diversity of experience, and social and professional perspectives are integrated at the coding stage.”

AI on the March, with Humans in the Loop.

The 2018 Global Human Capital Trends report from Deloitte Insights found that the influx of AI, robotics, and automation into the workplace has dramatically accelerated in the last year, and “uniquely human” skills and roles were found to be critically important. Skills seen to be in high demand in the future included complex problem-solving (63 percent), cognitive abilities (55 percent), and social skills (52 percent).

Reinforcing this view, a recent World Economic Forum study found that the top 10 skills for the next decade include essential human skills such as critical thinking, creativity, and people management.

To maximize the potential value of these new technologies today and minimize the potential adverse impacts on the workforce, organizations must put “humans in the loop” —reconstructing work, retraining people, and rearranging the organization. The greatest opportunity is not just to redesign jobs or automate routine work, but to fundamentally rethink “work architecture” to benefit organizations, teams, and individuals.

The Deloitte study found a “readiness gap” with 72 percent of respondents seeing AI as important and 31 percent reported being ready to address it.

Leading companies are recognizing that the technologies are more effective when used to complement and not replace humans. Manufacturers including Airbus and Nissan are finding ways to use collaborative robots, or “co-bots,” that work side by side with workers in factories.

An algorithm is only as effective as “the quantity and quality of the training data to get [it] going,” stated Lukas Biewald, CEO of CrowdFlower, a startup that provides algorithm trainers. This realization has given rise to new jobs with titles such as “bot trainer,” “bot farmer,” and “bot curator.”

Tell the Humans They are Not Fired

As AI technology is introduced and deployed, the workforce needs new skills to be able to exploit the new technologies. “Work architecture” needs to be redesigned. Work needs to be decomposed into it fundamental components  – for example production, problem-solving, communication, supervision – and ways that new combinations of humans and technology working together need to be defined.

Despite this recognition, the Deloitte study found companies are slow to develop the needed human skills of the future. Some 49 percent of respondents said they do not have a plan to cultivate them. “We see this as an urgent human capital challenge requiring top executive support to transform organizational structures, cultures, career options and performance management practices,” the report stated.

Further, “Absent a thoughtful approach, organizations may not only risk failing to identify the skills they need to take effective advantage of technology, but also suffer damage to their employee and corporate brand due to perceptions around (real or supposed) workforce reductions.”

The integration of early AI tools is also causing organizations to become more collaborative and team-oriented, to move away from traditional top-down hierarchical structures, according to an account in Fast Company..

“To integrate AI, you have to have an internal team of expert product people and engineers that know its application and are working very closely with the frontline teams that are actually delivering services,” stated Ian Crosby, co founder and CEO of Bench, a digital bookkeeping provider. “When we are working AI into our frontline service, we don’t go away to a dark room and come back after a year with our masterpiece. We work with our frontline bookkeepers day in, day out.”

Org Charts Moving Away from Top-Down, Towards Teams

The Deloitte survey also found organizations are moving away from a top-down structure and toward multidisciplinary teams. Some 32% of respondents said they are redesigning their organizations to be more team-centric, optimizing them for adaptability and learning in preparation for technological disruption.

Finding a balanced team structure, however, doesn’t happen overnight, Crosby suggested. In large organizations, “It’s better to start with a small team first, and let them evolve and scale up, rather than try to introduce the whole company all at once.”

Crosby adds that Bench’s eagerness to integrate new technologies also determines the skills the company seeks in recruiting and hiring. Beyond checking the boxes of the job’s technical requirements, he says the company looks for candidates that are ready to adapt to the changes that are coming.

“When you’re working with AI, you’re building things that nobody has ever built before, and nobody knows how that will look yet,” he said. “If they’re not open to being completely wrong, and having the humility to say they were wrong, we need to reevaluate.”

Where to Start

When building something never built before, where does one start? “This is one of those instances where getting started is more important than where to start,” suggests Trent Weier, a senior director with SAP who works with customers on projects, writing in Digitalist Magazine from SAP. “Building AI capabilities like machine learning is an evolutionary process and lends itself to short, focused discovery, design, prototyping, and delivery cycles.”

SAP has found early use case experience for AI and machine learning have seen benefits in process optimization, demand planning and forecast applications. The forecast algorithm, for example, evaluates errors for each cycle and recommends or automatically adapts the forecasting method to produce the best result.

For inventory applications, machine learning can automatically adjust optimal safety stock values and inventory parameters at each echelon of the supply chain. Multi-echelon inventory optimization (MEIO) strives to maintain the optimal balance of components, work in process, and finished goods inventory.

AI Impact on Daily Work Environment

AI stands to change the daily work environment, suggests a recent article in MIT Sloan Management Review.  “What people don’t talk about is the integration problem. Even if you can develop the system to do very focused, individual tasks for what people are doing today, as long as you can’t entirely remove the person from the process, you have a new problem that arises — which is coordinating the work of, even communication between, people and these AI systems,” stated Julie Shah, an associate professor of aeronautics at MIT. “And that interaction problem is still a very difficult problem for us, and it’s currently unsolved.”

The article is based on findings from the 2017 AI Global Executive Study and Research project conducted at MIT in partnership with Boston Consulting Group. The partners surveyed 3,000 business executives in the spring of 2017 from 112 countries and 21 industries, from organizations of various sizes, two-thirds of them outside the US.

While organizing for AI broadly, the enterprise will place a premium on soft skills and new forms of collaboration, including project teams composed of humans and machines.

Companies deploying AI are exploring many models of organization, with the Pioneers evenly split among centralized, distributed and hybrid organization models. The report suggests a hybrid model may make the most sense for large organizations, because companies need AI resources both centrally and locally. TIAA, for example, has an analytics center of excellence and a number of decentralized groups.

“The center of excellence is not intended to be the group that will provide all analytics for the entire organization. It provides expertise, guidance and direction to other internal teams that are working to deploy AI and analytics,” said J.D. Elliott, director of enterprise data management for TIAA, a Fortune 100 financial services organization with nearly $1 trillion of assets under management.

The message is not having all the answers is not a reason to hold back from where AI will take your organization.

— By John Desmond, AI Trends Editor, jd@aiworld.com

Here are 8 Myths About AI in the Workplace Debunked – With Infographic

By Jeff Desjardins, The Visual Capitalist The interplay between technology and work has always been a hot topic. While technology has typically created more jobs than it has destroyed on a historical basis, this context rarely stops people from believing that things are “different” this time around. In this case, it’s the potential impact of artificial intelligence […]

By Jeff Desjardins, The Visual Capitalist

The interplay between technology and work has always been a hot topic.

While technology has typically created more jobs than it has destroyed on a historical basis, this context rarely stops people from believing that things are “different” this time around.

In this case, it’s the potential impact of artificial intelligence (AI) that is being hotly debated by the media and expert commentators. Although there is no doubt that AI will be a transformative force in business, the recent attention on the subject has also led to many common misconceptions about the technology and its anticipated effects.

DISPROVING COMMON MYTHS ABOUT AI

Today’s infographic comes to us from Raconteur and it helps paint a clearer picture about the nature of AI, while attempting to debunk various myths about AI in the workplace.

AI is going to be a seismic shift in business – and it’s expected to create a $15.7 trillion economic impact globally by 2030.

But understandably, monumental shifts like this tend to make people nervous, resulting in many unanswered questions and misconceptions about the technology and what it will do in the workplace.

DEMYSTIFYING MYTHS

Here are the eight debunked myths about AI:

1. Automation will completely displace employees
Truth: 70% of employers see AI in supporting humans in completing business processes. Meanwhile, only 11% of employers believe that automation will take over the work found in jobs and business processes to a “great extent”.

2. Companies are primarily interested in cutting costs with AI
Truth: 84% of employers see AI as obtaining or sustaining a competitive advantage, and 75% see AI as a way to enter into new business areas. 63% see pressure to reduce costs as a reason to use AI.

3. AI, machine learning, and deep learning are the same thing 
Truth: AI is a broader term, while machine learning is a subset of AI that enables “intelligence” by using training algorithms and data. Deep learning is an even narrower subset of machine learning inspired by the interconnected neurons of the brain.

4. Automation will eradicate more jobs than it creates 
Truth: At least according to one recent study by Gartner, there will be 1.8 million jobs lost to AI by 2020 and 2.3 million jobs created. How this shakes out in the longer term is much more debatable.

5. Robots and AI are the same thing
Truth: Even though there is a tendency to link AI and robots, most AI actually works in the background and is unseen (think Amazon product recommendations). Robots, meanwhile, can be “dumb” and just automate simple physical processes.

6. AI won’t affect my industry 
Truth: AI is expected to have a significant impact on almost every industry in the next five years.

7. Companies implementing AI don’t care about workers
Truth: 65% of companies pursuing AI are also investing in the reskilling of current employees.

8. High productivity equals higher profits and less employment
Truth: AI and automation will increase productivity, but this could also translate to lower prices, higher wages, higher demand, and employment growth.

Read the source article at The Visual Capitalist.

Here Are the 3 Key Components of Artificial Intelligence Readiness

Artificial intelligence is the technology story of the hour, and everyone wants to dive in. However, three recent studies suggest there’s more work to be done before AI starts delivering business value. A report from McKinsey suggests many organizations require a solid infrastructure underneath it all — it takes digital to go more digital. Data is also […]

Artificial intelligence is the technology story of the hour, and everyone wants to dive in. However, three recent studies suggest there’s more work to be done before AI starts delivering business value.

A report from McKinsey suggests many organizations require a solid infrastructure underneath it all — it takes digital to go more digital. Data is also a vital piece of the puzzle, a survey of 2,300 executives from MIT Technology Review and PureStorage adds.

At the same time, KPMG reports that investment in AI technologies is still relatively low, though many companies have plans for future spending on the technology.

All three surveys find a high degree of optimism about AI. The majority of executives in the MIT survey, 81%, believe AI will have a positive impact on their industry in the future, and 64% are likely to consider investing in AI solutions in the future. In addition, 83% agree AI will significantly enhance processes across industries — such as self-driving
safety and improved healthcare.

These survey results identify three critical components to AI transformation:

Data. However, the MIT survey also sees the need to better govern and grasp data. At least 84% are concerned about the speed at which data can be received, interpreted and analyzed for AI systems. Another 83% also agree that it is essential that data is analyzed for meaning and context.

Infrastructure. The underlying technology is also a concern surfaced in the surveys. The McKinsey survey finds those with a strong digital base are most likely to succeed with their AI efforts. “It appears that AI adopters can’t flourish without a solid base of core and advanced digital technologies. Companies that can assemble this bundle of capabilities are starting to pull away from the pack and will probably be AI’s ultimate winners.”

Culture. The KPMG survey finds many organizations have digital and AI efforts that are too narrowly focused, rather than elevating those to a more strategic approach. “They have not positioned themselves to transform their business and operating models so they can become and remain competitive with digital-first companies,” the report notes.

“Organizations that can power up IA efforts can radically improve operations, transform their business models and become long-term winners,” the KPMG report adds. “But piecemeal efforts that focus mainly on cutting the cost of legacy processes and reducing headcount – with, for example, siloed efforts to automate payroll, invoice processing, and customer service inquiries – will not move the needle in this new world.” The report observes that taking a strategic approach to AI and related technologies can boost business returns of 5X to 10X.

The MIT researchers agree that it is still early in the story of AI, and it’s not too late to prepare organizations and their infrastructures for the AI wave. “Organizations that work to address AI challenges and educate workers at all levels on both the promise and the reality of AI, as well as the value of data, will derive the maximum value from their data stores—value that will drive better business performance and an optimal customer experience.”

Read the source article at RTInsights.

AI Tiptoes Into the Workplace, in the Beginning of a Wave

There is no shortage of predictions about how artificial intelligence is going to reshape where, how and if people work in the future. But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products. A more humble version of the technology, instead, is making its presence felt in […]

There is no shortage of predictions about how artificial intelligence is going to reshape where, how and if people work in the future.

But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products.

A more humble version of the technology, instead, is making its presence felt in a less glamorous place: the back office.

New software is automating mundane office tasks in operations like accounting, billing, payments and customer service.

The programs can scan documents, enter numbers into spreadsheets, check the accuracy of customer records and make payments with a few automated computer keystrokes.

The technology is still in its infancy, but it will get better, learning as it goes. So far, often in pilot projects focused on menial tasks, artificial intelligence is freeing workers from drudgery far more often than it is eliminating jobs.

The bots are mainly observing, following simple rules and making yes-or-no decisions, not making higher-level choices that require judgment and experience. “This is the least intelligent form of A.I.,” said Thomas Davenport, a professor of information technology and management at Babson College.

But all the signs point to much more to come. Big tech companies like IBM, Oracle and Microsoft are starting to enter the business, often in partnership with robotic automation start-ups. Two of the leading start-ups, UiPath and Automation Anywhere, are already valued at more than $1 billion. The market for the robotlike software will nearly triple by 2021, by one forecast.

“This is the beginning of a wave of A.I. technologies that will proliferate across the economy in the next decade,” said Rich Wong, a general partner at Accel, a Silicon Valley venture capital firm, and an investor in UiPath.

The emerging field has a klutzy name, “robotic process automation.” The programs — often called bots — fit into the broad definition of artificial intelligence because they use ingredients of A.I. technology, like computer vision, to do simple chores.

For many businesses, that is plenty. Nearly 60 percent of the companies with more than $1 billion in revenue have at least pilot programs underway using robotic automation, according to research from McKinsey & Company, the consulting firm.

The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, Mastercard, Manpower, FedEx, Cisco, Google, the Defense Department and NASA.

State Auto Insurance in Ohio Ahead of the Curve

State Auto Insurance Companies in Columbus, Ohio, started its first automation pilot project two years ago. Today, it has 30 software programs handling back-office tasks, with an estimated savings of 25,000 hours of human work — or the equivalent of about a dozen full-time workers — on an annualized basis, assuming a standard 2,000-hour work year.

Holly Uhl, a technology manager who leads the automation program, estimated that within two years the company’s bot population would double to 60 and its hours saved would perhaps triple to 75,000, nearly all in year-after-year savings rather than one-time projects.

Cutting jobs, Ms. Uhl said, is not the plan. The goal for the company, whose insurance offerings include auto, commercial and workers’ compensation, is to increase productivity and State Auto’s revenue with limited additions to its head count, she said.

Ms. Uhl said her message to workers is: “We’re here to partner with you to find those tasks that drive you crazy.”

Rebekah Moore, a premium auditor at the company, had one in mind. Premium auditors scrutinize insurance policies and make recommendations for changing rates. They audit less than half of the policies, Ms. Moore said.

The policies that will not be audited then have to be set aside and documented. That step, she explained, is a routine data entry task that involves fiddling with two computer programs, plugging in codes and navigating drop-down menus. It takes a minute or two. But because auditors handle many thousands of policies, the time adds up, to about an hour a day, she estimated.

Starting in May, a bot took over that chore. “No one misses that work,” Ms. Moore said.

Is she worried about the bots climbing up the task ladder to someday replace her? Not at all, she said. “We’ll find things to do with our time, higher-value work,” said Ms. Moore, 37.

On State Auto’s current path, her confidence seems justified. If the company hits its target of 75,000 hours in savings by 2020, that would be the equivalent of fewer than 40 full-time workers, compared with State Auto’s work force of 1,900. The company plans to grow in the next two years. If so, State Auto would most likely be hiring a few dozen people fewer than it would otherwise.

UiPath Envisions One Bot Per Employee

Automation companies are eager to promote the bots as helpful assistants instead of job killers. The technology, they say, will get smarter and more useful, liberating workers rather than replacing them.

“The long-term vision is to have one bot for every employee,” said Bobby Patrick, chief market officer for UiPath. The company, which is based in New York, recently reported that its revenue more than tripled in the first half of 2018, to a yearly rate of more than $100 million.

Mihir Shukla, chief executive of Automation Anywhere, refers to his company’s bots as “digital colleagues.” In July, the company announced it had raised a $250 million round of venture funding, valuing the company at $1.8 billion.

The market for A.I.-enhanced software automation is poised for rapid growth, but that expansion, analysts say, will ultimately bring job losses.

Forrester Research estimated that revenue would nearly triple to $2.9 billion over the next three years. And by 2021, robotic automation technology will be doing the equivalent work of nearly 4.3 million humans worldwide, Forrester predicted.

In a dynamic global labor market, that is not a clear-cut forecast of 4.3 million layoffs. The bots may do work not previously done by humans, and people may move onto new jobs.

“But these initial bots will get better, and the task harvesting will accelerate,” said Craig Le Clair, an analyst for Forrester. “For workers, there will be a mix of automation dividends and pain.”

The recent research has examined jobs as bundles of tasks, some of which seem ripe for replacement and others not. So the technology’s immediate impact will resemble the experience to date with robotic software, changing work more than destroying jobs.

For Ms. Uhl of State Auto, the most persistent pushback has come not at the company but at home, from her two young sons, Christian, 9, and Elijah, 7, who are eager to glimpse the future.

Hearing their mother talk about robots at work, they keep asking her to bring one home. “It’s not the kind of robot you can see,” Ms. Uhl said she has told her disappointed sons.

Read the source article in The New York Times.

Here are the Top 5 Languages for Machine Learning, Data Science

Careers in data science, artificial intelligence, machine learning, and related technologies are considered among the best choices to pursue in an uncertain future economy where many jobs may end up automated and performed by robots and AI. Yet in spite of the likely strong and secure future of these careers, the job marketplace remains fundamentally […]

Careers in data science, artificial intelligence, machine learning, and related technologies are considered among the best choices to pursue in an uncertain future economy where many jobs may end up automated and performed by robots and AI.

Yet in spite of the likely strong and secure future of these careers, the job marketplace remains fundamentally unbalanced. There are still many more jobs open and available than there are qualified applicants to fill those jobs. Just do a search on Monster for the keyword machine learning and you will find thousands of job openings across the country.

Whether you are just starting out in your IT career or you are watching high-profile IT layoffs and considering the best new skills to learn, chances are you are wondering what the best skills are to emphasize on your LinkedIn profile and the best skills to focus on in the next online course you take. What programming language is the most likely to secure your future?

Through our regular discussions with executives, recruiters, and practitioners in the field, we’ve come up with a short list for you. You may already have one or more of these skills. Maybe you are wondering about the best one to learn next. Here’s our list. If you see one that you think we missed, please let us know in the comments section.

R

R remains one of the top languages for data science. First developed in the 1990s, this open source language has its roots in statistics, data analysis, and data visualization. In recent years it’s become the choice of a new generation of analysts who have who have appreciated the active open source community, the fact that they can download the software for free, and the downloadable packages that are available to customize the tool. Tech giant Microsoft has also embraced the platform acquiring Revolution Analytics, a commercially supported enterprise platform for R, in 2015.

Java

Java has also been around since the early 1990s, and back then was famous for its “write once, run anywhere” design, originating inside Sun Microsystems. Sun may no longer exist, having been acquired by Oracle, but Java seems here to stay, and it’s one of the languages you will likely encounter in your career as a machine learning specialist. Many of the machine learning job description ads out there specify Java as one of the languages they’d like for you to know. Chances are if you’ve been in development at all over the last 20 years, you’ve acquired a little bit of experience with Java. And if you feel like you need a little more hands-on experience, it’s pretty easy to find an online course.

Scala

Scala is another language that has been popular with data scientists and machine learning specialists. You’ll see this one mentioned most often in job ads where real-time data analysis is important to the role. It is an implementation language of technologies that enable streaming data, such as Spark and Kafka. Scala combines functional and object-oriented programming and works with both Java and Javascript.

C and C++

These languages have also been around for decades, and you may see them mentioned in machine learning job ads in the same sentence as some other more popular languages for machine learning. Organizations may be looking to add machine learning to existing projects that were built in these languages and so they may be looking for this kind of expertise. But if you are looking for a first language to learn for use with machine learning, it’s probably not one of these.

Python

Right now, Python is probably the top language to learn if you are looking to skill up in areas around machine learning. Just check out online machine learning courses that are available today. Chances are the one you pick will be using Python as the language of choice.

You’ll also find that Python is probably the top named language skill in job ads for machine learning specialists, and certainly also mentioned in many ads for data scientists and analysts, too. If you have to choose one skill to learn this year, Python is a great choice.

Read the source article in InformationWeek.

What Machine Learning Practitioners Actually Do, by Rachel Thomas, fast.ai

By Rachel Thomas, founder, fast.ai and Asst. Professor, Data Institute, University of San Francisco There are frequent media headlines about both the scarcity of machine learning talent and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether. In his recent keynote at the TensorFlow DevSummit, Google’s head of AI Jeff […]

By Rachel Thomas, founder, fast.ai and Asst. Professor, Data Institute, University of San Francisco

There are frequent media headlines about both the scarcity of machine learning talent and about the promises of companies claiming their products automate machine learning and eliminate the need for ML expertise altogether. In his recent keynote at the TensorFlow DevSummit, Google’s head of AI Jeff Dean estimated that there are tens of millions of organizations that have electronic data that could be used for machine learning but lack the necessary expertise and skills. I follow these issues closely since my work at fast.ai focuses on enabling more people to use machine learning and on making it easier to use.

Rachel Thomas, founder, fast.ai

In thinking about how we can automate some of the work of machine learning, as well as how to make it more accessible to people with a wider variety of backgrounds, it’s first necessary to ask, what is it that machine learning practitioners do? Any solution to the shortage of machine learning expertise requires answering this question: whether it’s so we know what skills to teach, what tools to build, or what processes to automate.

This post is the first in a 3-part series. It will address what it is that machine learning practitioners do, with Part 2 explaining AutoML and neural architecture search (which several high profile figures have suggested will be key to decreasing the need for data scientists) and Part 3 will cover Google’s heavily-hyped AutoML product in particular.

Building Data Products is Complex Work

While many academic machine learning sources focus almost exclusively on predictive modeling, that is just one piece of what machine learning practitioners do in the wild. The processes of appropriately framing a business problem, collecting and cleaning the data, building the model, implementing the result, and then monitoring for changes are interconnected in many ways that often make it hard to silo off just a single piece (without at least being aware of what the other pieces entail). As Jeremy Howard et al. wrote in Designing Great Data Products, Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing.

A team from Google, D. Sculley et al., wrote the classic Machine Learning: The High-Interest Credit Card of Technical Debt, about the code complexity and technical debt often created when using machine learning in practice. The authors identify a number of system-level interactions, risks, and anti-patterns, including:

  • glue code: massive amount of supporting code written to get data into and out of general-purpose packages
  • pipeline jungles: the system for preparing data in an ML-friendly format may become a jungle of scrapes, joins, and sampling steps, often with intermediate files output
  • re-use input signals in ways that create unintended tight coupling of otherwise disjoint systems
  • risk that changes in the external world may make models or input signals change behavior in unintended ways, and these can be difficult to monitor

The authors write, A remarkable portion of real-world “machine learning” work is devoted to tackling issues of this form… It’s worth noting that glue code and pipeline jungles are symptomatic of integration issues that may have a root cause in overly separated “research” and “engineering” roles… It may be surprising to the academic community to know that only a tiny fraction of the code in many machine learning systems is actually doing “machine learning”. (emphasis mine)

When machine learning projects fail

In a previous post, I identified some failure modes in which machine learning projects are not effective in the workplace:

  • The data science team builds really cool stuff that never gets used. There’s no buy-in from the rest of the organization for what they’re working on, and some of the data scientists don’t have a good sense of what can realistically be put into production.
  • There is a backlog with data scientists producing models much faster than there is engineering support to put them in production.
  • The data infrastructure engineers are separate from the data scientists. The pipelines don’t have the data the data scientists are asking for now, and the data scientists are under-utilizing the data sources the infrastructure engineers have collected.
  • The company has definitely decided on feature/product X. They need a data scientist to gather some data that supports this decision. The data scientist feels like the PM is ignoring data that contradicts the decision; the PM feels that the data scientist is ignoring other business logic.
  • The data science team interviews a candidate with impressive math modeling and engineering skills. Once hired, the candidate is embedded in a vertical product team that needs simple business analytics. The data scientist is bored and not utilizing their skills.

I framed these as organizational failures in my original post, but they can also be described as various participants being overly focused on just one slice of the complex system that makes up a full data product. These are failures of communication and goal alignment between different parts of the data product pipeline.

So, what do machine learning practitioners do?

As suggested above, building a machine learning product is a multi-faceted and complex task. Here are some of the things that machine learning practitioners may need to do during the process:

Understanding the context:

  • identify areas of the business that could benefit from machine learning
  • communicate with other stakeholders about what machine learning is and is not capable of (there are often many misconceptions)
  • develop understanding of business strategy, risks, and goals to make sure everyone is on the same page
  • identify what kind of data the organization has
  • appropriately frame and scope the task
  • understand operational constraints (e.g. what data is actually available at inference time)
  • proactively identify ethical risks, including how your work could be mis-used by harassers, trolls, authoritarian governments, or for propaganda/disinformation campaigns (and plan how to reduce these risks)
  • identify potential biases and potential negative feedback loops

Read the source post at fast.ai.

How to Map Out a Career Path in This Early Stage of AI

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and […]

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology

Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and for years to come.

Stephanie Glass

Yet despite all the AI hype and initial successes, it remains in its infancy. That makes now the ideal time for young people to build the knowledge, skill sets and connections they need to capitalize on the fast-growing market for AI jobs and build a strong AI career.

One reason is simply practical. Gartner predicts that AI may eliminate 1.8 million jobs by 2020, yet is on track to create 2.3 million new positions. It clearly makes sense to map out an AI career path as new roles emerge that focus on problem-solving, collaboration and strategic decision-making.

The second reason is that AI offers a truly exciting opportunity to make an impact on our world. AI is an ideal career path for undergraduates and recent graduates with a passion for technology and an entrepreneurial spirit who relish a challenge and a front-line role in game-changing innovation.

AI has been called “the next Industrial Revolution,” and I think that analogy is spot on. The convergence of machine learning, big data and internet-scale cloud compute power is opening opportunities to transform healthcare, grow the economy, reduce waste, save energy, improve education, strengthen security and decrease poverty.

There’s virtually no area of society that will be untouched by AI. Given the myriad possibilities, how does a young person chart his or her course in the AI field?

Practical steps to an AI career

One first step is to simply narrow down key interests and strengths. AI offers technological roles in data science, machine learning development and architecting an AI technology stack. And it offers plenty of positions as a user or analyst with AI software, or in sales and marketing, HR, customer support and more.

Company type is another important consideration, as B2B and B2C can be distinctly different work environments. Candidates should also target industries they gravitate toward, be it technology, manufacturing, retail, higher education, services or others. To narrow down interests, hands-on research and learning is available through avenues such as:

AI and data science courses. Many free and low-cost online learning opportunities in AI can be found at providers such as Udacity, Coursera, Codeacademy and fast.ai. These courses are a great way to build knowledge and enrich a resume.

Industry and company conferences. AI experts and thought leaders discuss trends at conferences hosted by NIPS, Re-Work, O’Reilly, Gartner, AI World and others, while AI vendors also sponsor conferences. Students and young professionals are often able to gain free or discounted admission.

Meetup.com. The website lists many regional in-person meetups among AI enthusiasts, as well as online groups and discussions on topical areas including machine learning, Python, R, data science and advanced statistics.

General immersion. Keeping up-to-date on fast-moving AI news and trends pays off. So does using AI products, sharing code on GitHub or entering a code contest on the global Kaggle community. In short, would-be AI professionals should learn as much as they can, as fast as they can.

With this knowledge, young people are better able to address two key questions that can guide AI careers:

  1. What’s my prospective employer’s technology stack?
  2. What are the employer’s leadership motivations and potential social impact?

As a Stanford graduate now working in artificial intelligence, those were critical considerations for me as I looked to move from traditional software companies into the AI space. I had decided to pursue an AI career because of the positive change that AI promises to unleash. It was a matter of finding an employer that aligned with my interests and skills.

Read the source article in Entrepreneur.

How to Land a Data Scientist Job at Your Dream Company — My Journey to Airbnb

By Kelly Peng, Data Scientist, Airbnb I just started my new job at Airbnb as a data scientist a month ago, and I still feel that I’m too lucky to be here. Nobody knows how much I wanted to join this company — I had pictures of Airbnb office stuck in front of my desk; I had […]

By Kelly Peng, Data Scientist, Airbnb

I just started my new job at Airbnb as a data scientist a month ago, and I still feel that I’m too lucky to be here. Nobody knows how much I wanted to join this company — I had pictures of Airbnb office stuck in front of my desk; I had my iPhone wallpaper set as a photo of me standing in front of the Airbnb logo; I applied for positions at Airbnb four times, but only heard back from the recruiter in the last time…

Kelly Peng, data scientist, Airbnb

In the past, when people asked me, “Which company do you want to work for the most?” I dare not to say “Airbnb” because when I said that, they replied to me, “Do you know how many people want to work there? How many of them eventually got in? Come on, be realistic.”

The result proves that nothing is impossible. Since many of my friends asked me to share about my job search experience, I think it might be helpful to write it down and share with people.

Some data…

To provide an overview of my job search process:

  • Applications: 475
  • Phone interviews: 50
  • Finished data science take-home challenges: 9
  • Onsite interviews: 8
  • Offers: 2
  • Time spent: 6 months

As you probably can see from the data, I’m not a strong candidate because, otherwise, I would just apply for a few positions and receive a bunch of offers. Yes, I used to be super weak; I used to be the kind of candidates who are wasting interviewers’ time. But “who you were months ago doesn’t matter, what matters is who you are becoming.”

The road less traveled to a data scientist job

A little bit about my background, I obtained a Bachelor’s degree in Economics from a university in China and a Master’s degree in Business Administration from the University of Illinois at Urbana-Champaign. After graduated, I worked as a data analyst for two years, with 7 months as a contractor at Google, and another 1 year 4 months at a startup. My work was mostly about writing SQL queries, building dashboards, and give data-driven recommendations.

After realizing that I was not learning and growing as expected, I left my job and applied for Galvanize Data Science Immerse program, which is a 12-week boot camp in San Francisco. I failed the statistics interview to enter the boot camp program for 4 times, got admitted after taking the statistics interview for the fifth time.

The content taught at Galvanize was heavy on Python and machine learning, and they assume you already have a strong foundation in statistics. Unsurprisingly, I struggled a lot in the beginning, because I didn’t know much about programming, nor was I strong in statistics. I had no choice but to work really hard. During my time at Galvanize, I had no break, no entertainment, no dating, nothing else but more than 12 hours study every day. And I got much more comfortable with the courses later on.

However, I still embarrassed myself for uncountable times in interviews when I first started the job search process. Because the gap between a real data scientist and I was so huge that even though I was hardworking, the 12-week study was far from enough to make a career transformation. So I applied, interviewed, failed, applied again, interviewed again, failed again. The good thing is, each time I got to learn something new, and became a little bit stronger.

In March 2018, I have been unemployed for almost a year since I quitted my previous job. With only ~$600 in my bank account, I had no idea how to pay for the next month’s rent. What’s even worse, if I couldn’t find a job by the end of April 2018, I have to leave the U.S. because my visa will expire.

Luckily, after so much practice and repetition, I’ve grown from someone who doesn’t know how to introduce herself properly, doesn’t remember which one of Lasso and Ridge is L1, knows nothing about programming algorithms, into someone who knows she is ready to get what she wants.

When I entered the final interview at Airbnb, I had one data scientist offer in hand; thus, I was not nervous at all. My goal for the final interview was to be the best version of myself and leave no regret. The interview turned out to be the best one I have ever had. They gave me the offer, and all the hard work and sleepless nights paid off.

Read the source blog post at Toward Data Science.

Human Challenges Face Today’s AI Business Strategies

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace. However, that hasn’t stopped speculation about the impact on employment […]

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace. However, that hasn’t stopped speculation about the impact on employment and what it might mean for workers, especially those whose jobs are repetitive and considered low skilled.

In October last year,  a survey carried out by analytics giant Cary, N.C-based analytics giant SAS showed that the vast majority of organizations have begun to talk about artificial intelligence, and a few have even begun to implement suitable projects. There is much optimism about the potential of AI, although fewer were confident that their organization was ready to exploit that potential.

AI Human Challenges 

The reason for this is not because there is a lack of technologies on the market. What the research uncovered was that the challenges come from a shortage of data science skills to maximize value from emerging AI technology, and deeper organizational and societal obstacles to AI adoption. Some of the figures contained in the report show that:

  • 55 percent of survey respondents felt that the biggest challenge related to AI was the changing scope of human jobs in light of AI’s automation and autonomy.
  • 41 percent of respondents raised questions about whether robots and AI systems should have to work “for the good of humanity” rather than simply for a single company, and how to look after those who lost jobs to AI systems.

It also showed that several organizations had a senior-level sponsor for AI and advanced analytics. In some cases, this was a member of the C-suite, and in a few, the CEO. In others, it was a more junior director, usually one with an interest in the area. One respondent mentioned that the organization planned to appoint a Chief Data Officer within the next six months, who would take on responsibility for this area.

And it’s not the only research that has raised the issue of the impact AI will have on jobs. Recently, we were able to identify seven jobs that might be overtaken by the growth in the use of AI in the enterprise. That said there are ways that enterprises – and individuals – can meet the challenge.

Building a Talent Pipeline

AI is generating a demand for new skill sets in the workplace. However, there is a widespread shortage of talent that possess the knowledge and capabilities to properly build, fuel, and maintain these technologies within their organizations, according to Mohit Joshi president and head of banking, financial services and insurance, as well as, healthcare and life sciences at Bengalaru, India-based Infosys. The simple answer is up-skilling. “The lack of well-trained professionals who can build and direct a company’s AI and digital transformation journeys noticeably hinders progress and continues to be a major hurdle for businesses. But there is also opportunity here too and a way to redeploy workers who face redundancy because of AI,” he says.

To mitigate this, businesses should look inward and create on-the-job training and  to build these skills internally. With the proper staff powering AI, employees are able to focus on other critical activities and boost productivity creating a larger ROI. If an enterprise’s digital transformation goal is for AI to become a business accelerator, it needs to be an amplifier of its people. “It’s going to take work to give everyone access to the fundamental knowledge and skills in problem-finding and remove the elitism around advanced technology, but the boost to productivity and ROI will be worth it in the end,” says Joshi. Businesses that haven’t yet allocated budget for AI should start small by manually auditing the organization to streamline processes and free up employees’ bandwidth. This allows decision makers to clearly see which systems aren’t utilized effectively and which areas could benefit from technology down the road.

Shifting Roles

Anthony Macciola, Chief Innovation Officer and is responsible for AI initiatives at Moscow, Russia-based global giant ABBYY, a company that uses machine learning, robotic process automation and text analytics to improve business outcomes. He says that the introduction of AI into the general workplace will result in more tasks being addressed by system of record applications and shift knowledge workers’ roles from a control to an expertise standpoint.

He cites an example of how this will work in the mortgage lending market. The dependency on a loan origination officer to drive the loan process will diminish over time due to the loan origination system being able to make intelligent decisions based on past funding behavior. This will leave only rules-based exceptions to require a loan processor’s attention. As a result, this will lighten the overall workload for loan officers, allowing them to be more responsive when an exception rises and should allow mortgage lenders to increase the productivity of their operations.

“As software gets smarter, dependency on the workforce shrinks and knowledge workers who have typically conducted manual input tasks or controlled processes in fintech, healthcare, transportation and logistics, and government customer/constituent engagement scenarios will become more narrowly focused from a role and responsibility standpoint,” he says.

Read the source article at CMSWire.com.

Digital Strategies Are Passe: Time For An Artificial Intelligence Strategy

There’s nothing new about organizations and their leaders fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We’ve been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed. Now, such is the case […]

There’s nothing new about organizations and their leaders fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We’ve been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed. Now, such is the case with the constellation of cognitive solutions — artificial intelligence, machine learning and so forth — that are now starting to be embraced.

With AI and cognitive computing the flavor of the month (or year), it’s time to start exploring what, exactly, it can do for business growth, and how to go about achieving it. Some good news: AI isn’t quite as amorphous and squishy as digital. More good news: many of the bread-and-butter issues arising from previous generations of technology apply with AI as well — starting with the most fundamental of fundamental principles: don’t implement technology for technology’s sake, have a business goal in mind.

The not-so-good news is that a lot of money is starting to be poured into AI and cognitive technologies, vendors are hyper-ventilating about it, and analysts are telling us that if we don’t do it we will all be quickly put out of misery. So, with more and more money being invested in it, it’s really important to strategize things a bit more, to give it a broader purpose in the enterprise. As Thomas Davenport and Vikram Mahidhar, recently observed in MIT Sloan Management Review,  “few companies are yet getting value from their investments. Many of the projects companies undertake aren’t targeted at important business problems or opportunities. Most organizations don’t have a strategy for cognitive technologies.”

So what are the essential components of an AI strategy, or something close to a strategy. Here are a few pointers from leading voices in the field.

Step back and look at AI from an industry perspective: “Many companies that develop or provide AI to others have considerable strength in the technology itself and the data scientists needed to make it work, but they can lack a deep understanding of end markets,” as pointed out by Michael Chui and a team of fellow McKinsey analysts. Companies that seek to provide AI-driven offerings should not only analyze the value of their AI initiative, but also AI adoption across their industries.

Make AI about people and empowerment. AI may lead to many autonomous processes, but people will decide how it will drive the business. “The vision of AI should always be about empowering technical professionals and the business citizens to build a better user experience,” says Carlton Sapp, analyst with Gartner. “Technology cannot do this alone, and neither can AI. ”

Leverage data. This is the fuel that powers AI outputs. “Part of the reason machine learning has been so successful is that of its ability to train models based on data—as opposed to traditional methods that explicitly defined how the application would behave,” says Sapp. “Leveraging machine learning in your organization tells the world that you are truly data-driven.” Chui suggests building a “data plan” built on use cases and capable of producing “results and predictions, which can be fed either into designed interfaces for humans to act on or into transaction systems.” This includes mapping out how data is created, acquired, managed and delivered to AI engines.

Read the source article in Forbes.