Companies everywhere are pouring resources into artificial intelligence (A.I.) and machine learning (ML) initiatives. Many technologists believe that apps “smartened” with A.I. and ML tools will eventually offer better customer personalization; managers hope that A.I. will lead to better data analysis, which in turn will power better business strategies.

But which industries are actually hiring A.I. specialists? If you answer that question, it might give you a better idea of where those resources are being deployed. Fortunately, CompTIA’s latest Tech Jobs Report offers a breakdown of A.I. hiring, using data from Burning Glass, which collects and analyzes millions of job postings from across the country. Check it out:

Perhaps it’s no surprise that manufacturing tops this list; after all, manufacturers have been steadily automating their production processes for years, and it stands to reason that they would turn to A.I. and ML to streamline things even more. In theory, A.I. will also help manufacturers do everything from reducing downtime to improving supply chains—although it may take some time to get the models right. 

The presence of healthcare, banking, and public administration likewise seem logical. “These three industries have the money to invest in A.I. and ML right now and have the greatest opportunity to see the investment pay off, fast,” Gus Walker, director of product at Veritone, an A.I. tech company based in Costa Mesa, California, told Dice late last year.“That being said, the pandemic has caused industries hit the hardest to take a step back and look at how they can leverage AI and ML to rebuild or adjust in the new normal.”

Compared to overall tech hiring, the number of A.I.-related job postings is still relatively small. Right now, mastering and deploying A.I. and machine learning is something of a specialist industry; but as these technologies become more commodified, and companies develop tools that allow more employees to integrate A.I. and ML into their projects, the number of job postings for A.I. and ML positions could increase over the next several years. Indeed, one IDC report from 2020 found three-quarters of commercial enterprise applications could lean on A.I. in some way by 2021.

It’s also worth examining where all that A.I. hiring is taking place; it’s interesting that Washington DC tops this particular list, with New York City a close second; Silicon Valley and Seattle, the nation’s other big tech hubs, are somewhat further behind, at least for the moment. Washington DC is notable not only for federal government hiring, but the growing presence of companies such as Amazon that hunger for talent skilled in artificial intelligence:

Jobs that leverage artificial intelligence are potentially lucrative, with a current median salary (according to Burning Glass) of $105,000. It’s also a skill-set that more technologists may need to become familiar with, especially managers and executives. “A.I. is not going to replace managers but managers that use A.I. will replace those that do not,” Rob Thomas, senior vice president of IBM’s cloud and data platform, recently told CNBC. If you mention A.I. or ML on your resume and applications, make sure you know your stuff before the job interview; chances are good you’ll be tested on it. 

The post Which Industries are Hiring A.I. and Machine Learning Roles? appeared first on Artificial Intelligence.

This first blog post will focus on how Artificial Intelligence (AI) evolved throughout the years, I will mention and describe some of the milestones of this evolution and analyse how it became part of our lives and how it will become even more important in the future. Digital technology has been one of the fastest-growing tech in the history of mankind, today our phones have hundreds of GB of storage and process data faster than a computer would a few decades ago, the only other branches of technology that have developed this quickly are weapons. It is not rare to hear how computers were ‘as big as a room’ but one of the most interesting examples of the leap of digital technology, specifically information technology, dates back to 1969 when a computer with only 14kb of ram steered and landed the Saturn V and its human hosts on the moon. To put that into perspective, the blog that I am now writing will occupy more than 14kb of space in my storage, nowadays, we are used of talking about Megabytes or Gigabytes, or even Terabytes. Using kilobytes for everyday use would be like weighing a person in grams. As per computer technology, AI is similarly comparable when it comes to fast technological progression. Before discussing the different milestone that paved the way for modern AI it is important to understand what AI is. A machine has artificial intelligence if it can interpret data, potentially learn from that data, and use that knowledge to adapt and achieve specific goals or, as I saw in a YouTube comment, “AI is math that mimics what a human would do” (Singh, 2019, 100 likes).

The difference between AI and other machines is, in fact, the intelligence part; our laptops are incredibly good at storing and processing data, but they do not know much about interpreting that data, let alone learn from it. The story of AI begins in 1951 when the first AI programs were written. They were very simple programs; one played checkers and the other one played chess, the latter became more important since chess developed into the new ‘Moon Landing’ of the AI race. The next big milestone in machine learning would have occurred when a machine would be able to beat a human at chess, this occurred in 1956 when MANIAC (???) defeated a novice chess player at the Los Alamos Scientific Laboratory, very boring. So, the bar was moved upwards, now the new milestone was beating a world-class chess player, a Grandmaster, at his own game, humans, in general, were too easy to beat.

Meanwhile, the first AI program that could understand human language was created in 1964 and the following year the first chatbot, ELIZA, was invented. Fast forward 9 years and in 1974 the first autonomous vehicle was created. And then, 15 years later, the first autonomous vehicle using a neural network; a technique that performs machine learning by mimicking the network of neurons we have in our brain. This is all very interesting but back to the crucial milestone in AI, beating a Grandmaster at chess. For that the world had to wait until 1996 when Deep Blue, an AI created by IBM, defeated Garry Kasparov, the reigning world chess champion at the time, this version of Deep Blue calculated 100 million positions per second, which was still not enough to win the match, Kasparov won the three following games and drew twice. So, IBM went back to the drawing board and went for a second try a year later. The 1997 version of Deep Blue could calculate 200 million positions per second, twice as much as the year prior. This version of Deep Blue was made to win a match, a game was not enough anymore, and so it did. After a win, a loss and three draws Deep Blue crushed Kasparov in the overtime, winning game 6 and the match.

From there on the development grows exponentially, the more AI develops, the faster it develops. In 2002 iRobot introduces the Roomba, an autonomous vacuum cleaner, which is able to detect and avoid obstacles, mapping the environment it is cleaning for better efficiency, and always goes back to its charging post once it’s done cleaning. Two years later the U.S. military starts to consider the importance of autonomous vehicles and the Defence Advanced Research Projects Agency (DARPA) creates the DARPA Grand Challenge, a competition for autonomous vehicles. The latter will later develop into the Urban Challenge, created in 2007, in which autonomous vehicles are required to respect traffic laws and operate in an urban environment. In 2009 Google begins the development of its own autonomous vehicle. In 2011 virtual assistants like Siri and Cortana are implemented into various devices. Now, in 2020, Tesla already has a car that can drive itself, YouTube uses an AI to suggests videos based on our preferences, and Google ads are customized based on our tastes and DARPA has a program that can defeat fighter jet pilots in a simulated dogfight. AI is already a big part of our lives, Amazon suggest items to buy based on what is in our cart, Gmail filter e-mail by analysing the content (they have to work on that a bit more) Facebook is able to detect suicidal thinking patterns by analysing posts, Google knows what you are searching for even before you finish your sentence, Grammarly and Microsoft word help us in correcting and improving our essays based on what type of writing we are doing (academic, informal), what our audience is (knowledgeable or general) and what the focus is (Business, Technical).

In the upcoming years, AI will acquire more importance in our lives, the number of autonomous cars on the roads will grow exponentially, the number of times Siri will not understand what we’re saying will diminish as its ability to understand human language and thought will improve, maybe even our local bar will have robot barmen that prepare cocktails or suggest new beers based on our tastes. All of this will occur in a couple of decades maximum, AI technology is growing faster than ever now, and this growth will only become quicker.

What do you think about the future of AI? In your opinion is it a bright or an uncertain future that of the coexistence of humans and AI?

The post The Evolution of A.I. appeared first on Artificial Intelligence.

Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.

2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.

3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.

4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).

5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

A.I. in data cleansing

There are A.I. applications today that can weed out errors in master data. In fact, a business was able to reduce their data errors in incoming data by 94% through A.I. application in data error correction. Humans used to correct such errors before and the correction was based on knowledge about the product, data on similar products and so on. If a product is a liquid and the units of measure are missing, the human planner used to correct that gap looking at the product being replaced by this new product or other liquid products in the same price range and so on, or the image of the product itself. AI now does the same and has been seen to be able to correct 94% of data errors without human help.

A.I. in BI

We know when reports and dashboards are delivered, junior analysts typically do a pre-analysis to ascertain exceptions, and explanations for those exceptions. These analysts circle these exceptions and comment on the components contributing to the exception. They also may mark something as high priority if an urgent action is needed. This saves the decision maker’s time and presents them with distilled information. Today, A.I. is able to do this – augmenting junior analysts using anomaly detection and auto-drill down, pattern recognition and clustering.

A.I. in cognitive intelligence

The usual examples people think of when talking of AI applications is in cognitive intelligence. As an example, in this pandemic, cognitive systems are tracking masks wearing compliance in closed spaces, distancing norms compliance in factories and warehouses. Cognitive intelligence is also used in less known forms, there is a recent MIT report on an AI model that can detect Covid-19 infection from your cough, being incorporated into an FDA approved cell phone app.

A.I. in natural interaction

Chat bots are here to stay. If we haven’t interacted with one yet, we are probably not clicking on that chat button on most online shopping sites. It is interesting that A.I. learning is not being achieved in most chat bots from chat histories of those businesses alone, but using innovative data sources such as Q&A dialogues in published interviews, consumer panel discussions, even published plays, and so on.

The way personalisation is achieved is even more interesting. If A.I. was simply replacing a human agent the chat would begin with “how may I help you?”. However, A.I. is much more than that. There are call intent prediction models which learn from the behavior of customers who had a similar interaction, purchase history and profile. Gleaning from data, what did such cohorts mostly call about when they did call? So, the conversation starts with “Are you calling about your oximeter ordered yesterday?”

A.I. in expert systems

This is the area impacted most post pandemic. For example, demand forecasting of CPG products using traditional techniques became impossible. However, applying A.I. made a difference.

For example, loyalty card identifiers in transaction data helped A.I. learn that there are some products which saw an upswing in sales that would continue (because the same shoppers were re-purchasing high quantities). Products like soap, sanitizers, home cleaners, etc. were identified for continued upswing from such analysis. Other items that did see an upswing but were unlikely to sustain (because the same shopper was not repurchasing high quantities) were items like paper towels, pet food, baby food, etc. This kind of ability in the first two months of dynamic demand helped many firms to plan better for the supply and allocation across locations.

With digital interactions rising, application of A.I. for a cookie less world has led to Federated learning of cohorts (FLOCs). FLOCs use models learning from customer cohort behavior through distributed data, without transferring data to a central server, thus protecting privacy.

What has been true of times of upheaval, applies to this one as well. It is a time of opportunity for those who leverage available tools including A.I. to transform their business to gain competitive advantage. Others who delay, cannot afford to miss this revolution. They will be late and may struggle, if they sustain at all.

The post The critical role of A.I. in an enterprise today appeared first on Artificial Intelligence.