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DeepMind Shows How Expensive the AI Race Is Google’s Strategy to Win It

Can startups compete with BigTech when it comes to artificial intelligence?

love DeepMind, I think it’s one of the hottest talent beds in AI doing important work.

DeepMind’s achievements are dazzling in the realm of healthcare and game simulations and show new ways of training deep learning systems.

Indeed in March 2018, Google’s cloud business announced a new service that converts blocks of text into natural-sounding speech, the first product containing DeepMind code that’s for sale.

Alphabet operates other AI research groups, but DeepMind has been doing more futuristic work and is more R&D meets the future with things like Waymo or deep learning in Healthcare.

Let’s see now: $154 million in 2016, $341 million in 2017, $572 million in 2018.

DeepMind could save Waymo $billions ahead of its Waymo One product, and has already improved its pace in an industry that’s going to be huge even by 2026.

In the 21st century, I’d give DeepMind around a 3% probability of becoming something that could lead to artificial general intelligence (AGI).

As unlikely as we are to see it within our lifetimes, this is the sort of hub that you’d expect has the best chance.

In a nutshell, DeepMind has been putting most of its eggs in one basket, a technique known as deep reinforcement learning.

That technique combines deep learning, primarily used for recognizing patterns, with reinforcement learning, geared around learning based on reward signals, such as a score in a game or victory or defeat in a game like chess.

Quite funny how prominent training on video games has become then in this kind of deep learning.

That opened up the eyes of a lot of skeptics about the potential of this generation's iteration of machine learning swag.

Even as DeepMind continues to hire hundreds of expensive researchers and data scientists but isn’t generating any significant revenue, Google has what amounts to an unlimited budget on this particular goal.

We don’t even understand fully the end game of this company in five years’ time.

The company made headlines in 2016 after its AlphaGo program beat a human professional Go player, Lee Sedol, the world champion, in a five-game match, which was the subject of a documentary film.

10 Jobs Artificial Intelligence Will Replace (and 10 That Are Safe)

To give you an idea of what jobs might be most vulnerable and which might be safe, we've compiled lists of jobs AI can and can't replace based on advice from experts, stats from the website noted above, and other research.

AI can analyze sales calls far faster than any sales manager could -- in fact, it would take nine years of nonstop sales call analysis for a human being to compete, and that’s if they didn’t take vacation or sleep.

some jobs won’t be strictly replaced -- they just might be adjusted to account for new technologies’ “careers.” Based on the landmark 2013 study that inspired “Will Robots Take My Job?” we’ve rounded up some of the marketing and sales roles most likely to be replaced by robots, bots, and AI in the next few years.

This study analyzes the likely probability that a job will be replaced by automation and computerization -- based primarily on the level of routine a job has and the specialized training and social intelligence required to complete it.

Score: 99% Why: You probably already receive robo-calls on behalf of various products and services, and career growth in the telemarketing space is expected to decline by 3% by the year 2024.

Likelihood: 96% Why: Pam predicted this back on The Office, but in case you're not a fan, automated phone and scheduling systems can replace a lot of the traditional receptionist role -- especially at modern technology companies that don't have office-wide phone systems or multinational corporations.

Likelihood: 65% Why: The field is projected to grow 12% by 2024, but with so much content on the internet with instructions, step-by-step guides, and hacks out there, it's no surprise companies will rely more heavily on bots and automation to answer support questions from employees and customers in the future.

Likelihood: 54% Why: As advertising shifts away from print and TV and towards web and social media landscapes, people simply don't need to be managing those sales for marketers who want to buy ad space.

More social media platforms are making it easy for people to buy space through free application program interfaces (APIs) and self-serve ad marketplaces to remove the salesperson and make it faster and easier for users to make money -- and that's reflected in the projected 3% decline in the industry.

Companies are democratizing the shopping experience with features like self-checkout, and the modern buyer is much more internet-savvy and more likely to do internet research and make a buying decision on their own.1 Likelihood: 0.55% Why Not: It's kind of in the name -- but your company's Human Resources department will likely always need a human at the helm to manage interpersonal conflict with the help of non-cognitive and reasoning skills.

Likelihood: 1.3% Why Not: Sales managers need a high level of emotional intelligence to hit their quotas each month, network and collaborate with customers, and motivate and encourage the larger sales team.

PR managers who have to raise awareness around an issue or mission need a particularly human touch to raise funds or get people to participate in a campaign, too -- and jobs are expected to grow 7% by 2024.

Likelihood: 3.7% Why Not: Event planning is a growing field, and if you ask anyone on our events team here at HubSpot, whether you're planning an event for employees, customers, or an industry event with tens of thousands of attendees, the planning process has many, many moving parts involved.

AIs can do some of this with title suggestions, writing prompts, and automated social media messages, but blog posts, books, movies, and plays will likely be written by humans for the foreseeable future.

Likelihood: 4.2% Why Not: Software engineering and development is hard enough for human beings to do, and the time and skill investment needed to create applications, software, and websites will be tough to replicate -- especially since developers need to execute perfectly to create great products for customers.

Likelihood: 5.5% Why Not: While some of the load can be lifted from editors with the automated proofreading technology mentioned previously, editors have to review writers' submission for clarity, accuracy, comprehensiveness, and originality.

Likelihood: 8.2% Why Not: Although there are some AIs taking small (and somewhat creepy) steps in the graphic design space, graphic design is both artistic and technical, making it an ideal role for a human being to carry out.

'As an example, today consultants help companies know what to do, when to do it and how to do it but soon AI-powered insights and recommendations software will give marketers their to-do list and that list is prioritized based on their specific goals,' he explains.

'This is going to be a continuously moving target, but for the time being, what AI can't do well is use emotional intelligence, understand situational context, make judgment calls, and generally see nuance and meaning like we do,' she says.

DeepMind's Losses and the Future of Artificial Intelligence

DeepMind, likely the world’s largest research-focused artificial intelligence operation, is losing a lot of money fast, more than $1 billion in the past three years.

Certainly, genuine machine intelligence (also known as artificial general intelligence), of the sort that would power a Star Trek–like computer, capable of analyzing all sorts of queries posed in ordinary English, would be worth far more than that.

That technique combines deep learning, primarily used for recognizing patterns, with reinforcement learning, geared around learning based on reward signals, such as a score in a game or victory or defeat in a game like chess.

DeepMind gave the technique its name in 2013, in an exciting paper that showed how a single neural network system could be trained to play different Atari games, such as Breakout and Space Invaders, as well as, or better than, humans.

DeepMind’s StarCraft outcomes were similarly limited, with better-than-human results when played on a single map with a single “race” of character, but poorer results on different maps and with different characters.

(DeepMind’s recent results with kidney disease have been questioned in similar ways.) Deep reinforcement learning also requires a huge amount of data—e.g., millions of self-played games of Go.

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