Robo retail: The automated store of the future is heading closer to our doorsteps. — from jwtintelligence.com’

Excerpt:

The automated store of the future is heading closer to our doorsteps.

Self-checkout and online delivery services might soon be outmoded. Automated, cashier-less and mobile, doorstep-accessible shopping outlets are popping up globally—these offer not only a quick and seamless shopping experience, but also allow customers to handpick the items they are seeking.

Retail giant Amazon launched its Amazon Go store in Seattle in late January 2018. Amazon Go stocks everyday items, Whole Foods Market goods and Amazon-branded meal kits, but has no cashiers, no check-out lines and no barcode scanners. Shoppers enter by scanning an app, shop and leave—items purchased are automatically charged to their accounts. Dilip Kumar, vice president of technology for Amazon Go, suggests the concept is Amazon’s answer to solving “time poverty,” which he calls people’s “number one problem.”

This is just the tip of the iceberg. While Amazon Go currently only operates in Seattle, two other mobile concepts are hoping to reach a wider audience by physically bringing roving stores directly to the consumer. Robomart, based in the Bay Area, is a prototype tap-to-request grab-and-go food mart. Conventional grocery delivery services like those run by Amazon, FreshDirect or Instacart don’t let customers select products for themselves. If you’re particular about the ripeness of an avocado or conscious about bruises on tomatoes, being able to choose your own produce is essential. An autonomy-focused platform like Robomart puts consumers in the metaphorical driver’s seat, while still maintaining a high level of ease.

 

 

 

From DSC:
Speaking of cashiers, I had some comments regarding the future of cashiers towards the bottom of this posting here.  Another relevant posting is: “Tech companies should stop pretending AI won’t destroy jobs” + 6 other items re: AI, bots, algorithms, & more

 

 

 

Tech companies should stop pretending AI won’t destroy jobs — from technologyreview.com / MIT Technology Review by Kai-Fu Lee
No matter what anyone tells you, we’re not ready for the massive societal upheavals on the way.

Excerpt (emphasis DSC):

The rise of China as an AI superpower isn’t a big deal just for China. The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere. The change will be massive, and not all of it good. Inequality will widen. As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent. Consider the progress of Google DeepMind’s AlphaGo software, which beat the best human players of the board game Go in early 2016. It was subsequently bested by AlphaGo Zero, introduced in 2017, which learned by playing games against itself and within 40 days was superior to all the earlier versions. Now imagine those improvements transferring to areas like customer service, telemarketing, assembly lines, reception desks, truck driving, and other routine blue-collar and white-­collar work. It will soon be obvious that half of our job tasks can be done better at almost no cost by AI and robots. This will be the fastest transition humankind has experienced, and we’re not ready for it.

And finally, there are those who deny that AI has any downside at all—which is the position taken by many of the largest AI companies. It’s unfortunate that AI experts aren’t trying to solve the problem. What’s worse, and unbelievably selfish, is that they actually refuse to acknowledge the problem exists in the first place.

These changes are coming, and we need to tell the truth and the whole truth. We need to find the jobs that AI can’t do and train people to do them. We need to reinvent education. These will be the best of times and the worst of times. If we act rationally and quickly, we can bask in what’s best rather than wallow in what’s worst.

 

From DSC:
If a business has a choice between hiring a human being or having the job done by a piece of software and/or by a robot, which do you think they’ll go with? My guess? It’s all about the money — whichever/whomever will be less expensive will get the job.

However, that way of thinking may cause enormous social unrest if the software and robots leave human beings in the (job search) dust. Do we, as a society, win with this way of thinking? To me, it’s capitalism gone astray. We aren’t caring enough for our fellow members of the human race, people who have to put bread and butter on their tables. People who have to support their families. People who want to make solid contributions to society and/or to pursue their vocation/callings — to have/find purpose in their lives.

 

Others think we’ll be saved by a universal basic income. “Take the extra money made by AI and distribute it to the people who lost their jobs,” they say. “This additional income will help people find their new path, and replace other types of social welfare.” But UBI doesn’t address people’s loss of dignity or meet their need to feel useful. It’s just a convenient way for a beneficiary of the AI revolution to sit back and do nothing.

 

 

To Fight Fatal Infections, Hospitals May Turn to Algorithms — from scientificamerican.com by John McQuaid
Machine learning could speed up diagnoses and improve accuracy

Excerpt:

The CDI algorithm—based on a form of artificial intelligence called machine learning—is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.

“The implications of machine learning are profound,” Syed said. “Yet it also promises to be an unpredictable, disruptive force—likely to alter the way medical decisions are made and put some people out of work.

 

 

Lawyer-Bots Are Shaking Up Jobs — from technologyreview.com by Erin Winick

Excerpt:

Meticulous research, deep study of case law, and intricate argument-building—lawyers have used similar methods to ply their trade for hundreds of years. But they’d better watch out, because artificial intelligence is moving in on the field.

As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer’s job and 35 percent of a law clerk’s job can be automated, which means that while humanity won’t be completely overtaken, major businesses and career adjustments aren’t far off (see “Is Technology About to Decimate White-Collar Work?”). In some cases, they’re already here.

 

“If I was the parent of a law student, I would be concerned a bit,” says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago. “There are fewer opportunities for young lawyers to get trained, and that’s the case outside of AI already. But if you add AI onto that, there are ways that is advancement, and there are ways it is hurting us as well.”

 

So far, AI-powered document discovery tools have had the biggest impact on the field. By training on millions of existing documents, case files, and legal briefs, a machine-learning algorithm can learn to flag the appropriate sources a lawyer needs to craft a case, often more successfully than humans. For example, JPMorgan announced earlier this year that it is using software called Contract Intelligence, or COIN, which can in seconds perform document review tasks that took legal aides 360,000 hours.

People fresh out of law school won’t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its “virtual associate,” CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer’s arguments.

 

 

Lessons From Artificial Intelligence Pioneers — from gartner.com by Christy Pettey

CIOs are struggling to accelerate deployment of artificial intelligence (AI). A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. However, the survey also found that one-fifth of the CIOs are already piloting or planning to pilot AI in the short term.

Such ambition puts these leaders in a challenging position. AI efforts are already stressing staff, skills, and the readiness of in-house and third-party AI products and services. Without effective strategic plans for AI, organizations risk wasting money, falling short in performance and falling behind their business rivals.

Pursue small-scale plans likely to deliver small-scale payoffs that will offer lessons for larger implementations

“AI is just starting to become useful to organizations but many will find that AI faces the usual obstacles to progress of any unproven and unfamiliar technology,” says Whit Andrews, vice president and distinguished analyst at Gartner. “However, early AI projects offer valuable lessons and perspectives for enterprise architecture and technology innovation leaders embarking on pilots and more formal AI efforts.”

So what lessons can we learn from these early AI pioneers?

 

 

Why Artificial Intelligence Researchers Should Be More Paranoid — from wired.com by Tom Simonite

Excerpt:

What to do about that? The report’s main recommendation is that people and companies developing AI technology discuss safety and security more actively and openly—including with policymakers. It also asks AI researchers to adopt a more paranoid mindset and consider how enemies or attackers might repurpose their technologies before releasing them.

 

 

How to Prepare College Graduates for an AI World — from wsj.com by
Northeastern University President Joseph Aoun says schools need to change their focus, quickly

Excerpt:

WSJ: What about adults who are already in the workforce?

DR. AOUN: Society has to provide ways, and higher education has to provide ways, for people to re-educate themselves, reskill themselves or upskill themselves.

That is the part that I see that higher education has not embraced. That’s where there is an enormous opportunity. We look at lifelong learning in higher education as an ancillary operation, as a second-class operation in many cases. We dabble with it, we try to make money out of it, but we don’t embrace it as part of our core mission.

 

 

Inside Amazon’s Artificial Intelligence Flywheel — from wired.com by Steven Levy
How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company.

Excerpt:

Amazon loves to use the word flywheel to describe how various parts of its massive business work as a single perpetual motion machine. It now has a powerful AI flywheel, where machine-learning innovations in one part of the company fuel the efforts of other teams, who in turn can build products or offer services to affect other groups, or even the company at large. Offering its machine-learning platforms to outsiders as a paid service makes the effort itself profitable—and in certain cases scoops up yet more data to level up the technology even more.

 

 

 

 

Fake videos are on the rise. As they become more realistic, seeing shouldn’t always be believing — from latimes.com by David Pierson Fe

Excerpts:

It’s not hard to imagine a world in which social media is awash with doctored videos targeting ordinary people to exact revenge, extort or to simply troll.

In that scenario, where Twitter and Facebook are algorithmically flooded with hoaxes, no one could fully believe what they see. Truth, already diminished by Russia’s misinformation campaign and President Trump’s proclivity to label uncomplimentary journalism “fake news,” would be more subjective than ever.

The danger there is not just believing hoaxes, but also dismissing what’s real.

The consequences could be devastating for the notion of evidentiary video, long considered the paradigm of proof given the sophistication required to manipulate it.

“This goes far beyond ‘fake news’ because you are dealing with a medium, video, that we traditionally put a tremendous amount of weight on and trust in,” said David Ryan Polgar, a writer and self-described tech ethicist.

 

 

 

 

From DSC:
Though I’m typically pro-technology, this is truly disturbing. There are certainly downsides to technology as well as upsides — but it’s how we use a technology that can make the real difference. Again, this is truly disturbing.

 

 

What is Artificial Intelligence, Machine Learning and Deep Learning — from geospatialworld.net by Meenal Dhande

 

 

 

 

 


 

What is the difference between AI, machine learning and deep learning? — from geospatialworld.net by Meenal Dhande

Excerpt:

In the first part of this blog series, we gave you simple and elaborative definitions of what is artificial intelligence (AI), machine learning and deep learning. This is the second part of the series; here we are elucidating our readers with – What is the difference between AI, machine learning, and deep learning.

You can think of artificial intelligence (AI), machine learning and deep learning as a set of a matryoshka doll, also known as a Russian nesting doll. Deep learning is a subset of machine learning, which is a subset of AI.

 

 

 

 

 


Chatbot for College Students: 4 Chatbots Tips Perfect for College Students — from chatbotsmagazine.com by Zevik Farkash

Excerpts:

1. Feed your chatbot with information your students don’t have.
Your institute’s website can be as elaborate as it gets, but if your students can’t find a piece of information on it, it’s as good as incomplete. Say, for example, you offer certain scholarships that students can voluntarily apply for. But the information on these scholarships are tucked away on a remote page that your students don’t access in their day-to-day usage of your site.

So Amy, a new student, has no idea that there’s a scholarship that can potentially make her course 50% cheaper. She can scour your website for details when she finds the time. Or she can ask your university’s chatbot, “Where can I find information on your scholarships?”

And the chatbot can tell her, “Here’s a link to all our current scholarships.”

The best chatbots for colleges and universities tend to be programmed with even more detail, and can actually strike up a conversation by saying things like:

“Please give me the following details so I can pull out all the scholarships that apply to you.
“Which department are you in? (Please select one.)
“Which course are you enrolled in? (Please select one.)
“Which year of study are you in? (Please select one.)
“Thank you for the details! Here’s a list of all applicable scholarships. Please visit the links for detailed information and let me know if I can be of further assistance.”

2. Let it answer all the “What do I do now?” questions.

3. Turn it into a campus guide.

4. Let it take care of paperwork.

 

From DSC:
This is the sort of thing that I was trying to get at last year at the NGLS 2017 Conference:

 

 

 

 


18 Disruptive Technology Trends For 2018 — from disruptionhub.com by Rob Prevett

Excerpts:

1. Mobile-first to AI-first
A major shift in business thinking has placed Artificial Intelligence at the very heart of business strategy. 2017 saw tech giants including Google and Microsoft focus on an“AI first” strategy, leading the way for other major corporates to follow suit. Companies are demonstrating a willingness to use AI and related tools like machine learning to automate processes, reduce administrative tasks, and collect and organise data. Understanding vast amounts of information is vital in the age of mass data, and AI is proving to be a highly effective solution. Whilst AI has been vilified in the media as the enemy of jobs, many businesses have undergone a transformation in mentalities, viewing AI as enhancing rather than threatening the human workforce.

7. Voice based virtual assistants become ubiquitous
Google HomeThe wide uptake of home based and virtual assistants like Alexa and Google Home have built confidence in conversational interfaces, familiarising consumers with a seamless way of interacting with tech. Amazon and Google have taken prime position between brand and customer, capitalising on conversational convenience. The further adoption of this technology will enhance personalised advertising and sales, creating a direct link between company and consumer.

 


 

5 Innovative Uses for Machine Learning — from entrepreneur.com
They’ll be coming into your life — at least your business life — sooner than you think.

 


 

Philosophers are building ethical algorithms to help control self-driving cars – from qz.com by Olivia Goldhill

 


 

Tech’s Ethical ‘Dark Side’: Harvard, Stanford and Others Want to Address It — from nytimes.com by Natasha Singerfeb

Excerpt:

PALO ALTO, Calif. — The medical profession has an ethic: First, do no harm.

Silicon Valley has an ethos: Build it first and ask for forgiveness later.

Now, in the wake of fake news and other troubles at tech companies, universities that helped produce some of Silicon Valley’s top technologists are hustling to bring a more medicine-like morality to computer science.

This semester, Harvard University and the Massachusetts Institute of Technology are jointly offering a new course on the ethics and regulation of artificial intelligence. The University of Texas at Austin just introduced a course titled “Ethical Foundations of Computer Science” — with the idea of eventually requiring it for all computer science majors.

And at Stanford University, the academic heart of the industry, three professors and a research fellow are developing a computer science ethics course for next year. They hope several hundred students will enroll.

The idea is to train the next generation of technologists and policymakers to consider the ramifications of innovations — like autonomous weapons or self-driving cars — before those products go on sale.

 


 

 

 

From DSC:
Here’s a quote that has been excerpted from the announcement below…and it’s the type of service that will be offered in our future learning ecosystems — our next generation learning platforms:

 

Career Insight™ enables prospective students to identify programs of study which can help them land the careers they want: Career Insight™ describes labor market opportunities associated with programs of study to prospective students. The recommendation engine also matches prospective students to programs based on specific career interests.

 

But in addition to our future learning platforms pointing new/prospective students to physical campuses, the recommendation engines will also provide immediate access to digital playlists for the prospective students/learners to pursue from their living rooms (or as they are out and about…i.e., mobile access).

 

The Living [Class] Room -- by Daniel Christian -- July 2012 -- a second device used in conjunction with a Smart/Connected TV

 

 

Artificial intelligence working with enormous databases to build/update recommendation engines…yup, I could see that. Lifelong learning. Helping people know what to reinvent themselves to.

 

 


 

Career Insight™ Lets Prospective Students Connect Academic Program Choices to Career Goals — from burning-glass.com; also from Hadley Dreibelbis from Finn Partners
New Burning Glass Technologies Product Brings Job Data into Enrollment Decisions

BOSTON—Burning Glass Technologies announces the launch of Career Insight™, the first tool to show prospective students exactly how course enrollment will advance their careers.

Embedded in institutional sites and powered by Burning Glass’ unparalleled job market data, Career Insight’s personalized recommendation engine matches prospective students with programs based on their interests and goals. Career Insight will enable students to make smarter decisions, as well as improve conversion and retention rates for postsecondary institutions.

“A recent Gallup survey found that 58% of students say career outcomes are the most important reason to continue their education,” Burning Glass CEO Matthew Sigelman said. “That’s particularly true for the working learners who are now the norm on college campuses. Career Insight™ is a major step in making sure that colleges and universities can speak their language from the very first.”

Beginning an educational program with a firm, realistic career goal can help students persist in their studies. Currently only 29% of students in two-year colleges and 59% of those in four-year institutions complete their degrees within six years.

Career Insight™ enables prospective students to identify programs of study which can help them land the careers they want:

  • Career Insight™ describes labor market opportunities associated with programs of study to prospective students. The recommendation engine also matches prospective students to programs based on specific career interests.
  • The application provides insights to enrollment, advising, and marketing teams into what motivates prospective students, analysis that will guide the institution in improving program offerings and boosting conversion.
  • Enrollment advisors can also walk students through different career and program scenarios in real time.

Career Insight™ is driven by the Burning Glass database of a billion job postings and career histories, collected from more than 40,000 online sources daily. The database, powered by a proprietary analytic taxonomy, provides insight into what employers need much faster and in more detail than any other sources.

Career Insight™ is powered by the same rich dataset Burning Glass delivers to hundreds of leading corporate and education customers – from Microsoft and Accenture to Harvard University and Coursera.

More information is available at http://burning-glass.com/career-insight.

 


 

 

You can now build Amazon Music playlists using voice commands on Alexa devices — from theverge.com by Natt Garun

Excerpt:

Amazon today announced that Amazon Music listeners can now build playlists using voice commands via Alexa. For example, if they’re streaming music from an app or listening to the radio on an Alexa-enabled device, they can use voice commands to add the current song to a playlist, or start a new playlist from scratch.

 

From DSC:
I wonder how long it will be before we will be able to create and share learning-based playlists for accessing digitally-based resources…? Perhaps AI will be used to offer a set of playlists on any given topic…?

With the exponential pace of change that we’re starting to experience — plus the 1/2 lives of information shrinking — such features could come in handy.

 

 

 

 

 

The Implications of Gartner’s Top 10 Tech Trends of 2018 for Education — from gettingsmart.com by Jim Goodell, Liz Glowa and Brandt Redd

Excerpt:

In October, Gartner released a report with predictions about the top tech trends for business in 2018. Gartner uses the term the intelligent digital mesh to describe “the entwining of people, devices, content and services” that will create the “foundation for the next generation of digital business models and ecosystems.” These trends are classified within three categories.

  • Intelligent: How AI is seeping into virtually every technology and with a defined, well-scoped focus can allow more dynamic, flexible and potentially autonomous systems.
  • Digital: Blending the virtual and real worlds to create an immersive digitally enhanced and connected environment.
  • Mesh: The connections between an expanding set of people, business, devices, content and services to deliver digital outcomes.

What are the implications of these trends for education?
Education often falls behind the business world in realizing the potential of new technologies. There are however a few bright spots where the timing might be right for the tech trends in the business world to have a positive impact in education sooner rather than later.

The top 10 trends according to Gartner are analyzed below for their implications for education…

1) Artificial Intelligence Foundation
2) Intelligent Apps and Analytics
3) Intelligent Things

 

 

 

AI plus human intelligence is the future of work — from forbes.com by Jeanne Meister

Excerpts:

  • 1 in 5 workers will have AI as their co worker in 2022
  • More job roles will change than will be become totally automated so HR needs to prepare today


As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well. Forward looking HR leaders are piloting chatbots now to transform HR, and, in the process, re-imagine, re-invent, and re-tool the employee experience.

How does all of this impact HR in your organization? The following ten HR trends will matter most as AI enters the workplace…

The most visible aspect of how HR is being impacted by artificial intelligence is the change in the way companies source and recruit new hires. Most notably, IBM has created a suite of tools that use machine learning to help candidates personalize their job search experience based on the engagement they have with Watson. In addition, Watson is helping recruiters prioritize jobs more efficiently, find talent faster, and match candidates more effectively. According to Amber Grewal, Vice President, Global Talent Acquisition, “Recruiters are focusing more on identifying the most critical jobs in the business and on utilizing data to assist in talent sourcing.”

 

…as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience.

 

 

From DSC:
Although I like the possibility of using machine learning to help employees navigate their careers, I have some very real concerns when we talk about using AI for talent acquisition. At this point in time, I would much rather have an experienced human being — one with a solid background in HR — reviewing my resume to see if they believe that there’s a fit for the job and/or determine whether my skills transfer over from a different position/arena or not. I don’t think we’re there yet in terms of developing effective/comprehensive enough algorithms. It may happen, but I’m very skeptical in the meantime. I don’t want to be filtered out just because I didn’t use the right keywords enough times or I used a slightly different keyword than what the algorithm was looking for.

Also, there is definitely age discrimination occurring out in today’s workplace, especially in tech-related positions. Folks who are in tech over the age of 30-35 — don’t lose your job! (Go check out the topic of age discrimination on LinkedIn and similar sites, and you’ll find many postings on this topic — sometimes with 10’s of thousands of older employees adding comments/likes to a posting). Although I doubt that any company would allow applicants or the public to see their internally-used algorithms, how difficult would it be to filter out applicants who graduated college prior to ___ (i.e., some year that gets updated on an annual basis)? Answer? Not difficult at all. In fact, that’s at the level of a Programming 101 course.

 

 

 

Artificial intelligence is going to supercharge surveillance – from theverge.com by James Vincent
What happens when digital eyes get the brains to match?

From DSC:
Persons of interest” comes to mind after reading this article. Persons of interest is a clever, well done show, but still…the idea of combining surveillance w/ a super intelligent is a bit unnerving.

 

 

 

Artificial intelligence | 2018 AI predictions — from thomsonreuters.com

Excerpts:

  • AI brings a new set of rules to knowledge work
  • Newsrooms embrace AI
  • Lawyers assess the risks of not using AI
  • Deep learning goes mainstream
  • Smart cars demand even smarter humans
  • Accountants audit forward
  • Wealth managers look to AI to compete and grow

 

 

 

Chatbots and Virtual Assistants in L&D: 4 Use Cases to Pilot in 2018 —  from bottomlineperformance.com by Steven Boller

Excerpt:

  1. Use a virtual assistant like Amazon Alexa or Google Assistant to answer spoken questions from on-the-go learners.
  2. Answer common learner questions in a chat window or via SMS.
  3. Customize a learning path based on learners’ demographic information.
  4. Use a chatbot to assess learner knowledge.

 

 

 

Suncorp looks to augmented reality for insurance claims — from itnews.com.au by Ry Crozier with thanks to Woontack Woo for this resource

Excerpts:

Suncorp has revealed it is exploring image recognition and augmented reality-based enhancements for its insurance claims process, adding to the AI systems it deployed last year.

The insurer began testing IBM Watson software last June to automatically determine who is at fault in a vehicle accident.

“We are working on increasing our use of emerging technologies to assist with the insurance claim process, such as using image recognition to assess type and extent of damage, augmented reality that would enable an off-site claims assessor to discuss and assess damage, speech recognition, and obtaining telematic data from increasingly automated vehicles,” the company said.

 

 

 

6 important AI technologies to look out for in 2018 — from itproportal.com by  Olga Egorsheva
Will businesses and individuals finally make AI a part of their daily lives?

 

 

 

 

 
 

The AI glossary: 5 artificial intelligence terms you need to know — from techradar.com by David Nield

Excerpt:

Algorithms
Ah, the famous (or infamous) algorithm. Algorithms are sets of rules that computer programs can follow, so if one of your best friends posts a photo of you on Facebook, then the rules say that should go up at the top of your News Feed. Or if you need to get from A to B on Google Maps, an algorithm can help you work out the fastest route.

The rules are followed by computers but usually set by humans – so it’s the Facebook engineers who choose what makes a story important or which roads are fastest. Where AI starts to come in is in tweaking these algorithms using machine learning, so programs begin to adapt these rules for themselves. Google Maps might do this if it starts getting feedback data that a particular road is shut.

When image recognition systems get it wrong, for example, that’s an example of an algorithm or set of rules at work – the same rules have been applied but the wrong result has been reached, so you get a cat-like dog rather than an actual cat. In many ways, algorithms are the building blocks of machine learning.

 

Deep learning
Deep learning is a type or a subset of machine learning, which is why the two terms often get jumbled up, and can correctly be used to describe the same AI in a lot of cases. It’s machine learning but designed to be even more intelligent, with more nuance and more layers, and intended to work more like the human brain does.

Deep learning has been made possible by two key technological advances: more data and more powerful hardware. That’s why it’s only recently come into fashion, though its original roots go back decades. If you think about it as machine learning turned up to 11, you can understand why it’s getting smarter as computers get more powerful.

Deep learning often makes use of neural networks to add this extra layer of intelligence. For example, both deep learning and machine learning can recognize a cat in a picture by scanning a million cat images – but whereas machine learning needs to be told what features make up a cat, deep learning can work out what a cat looks like for itself, as long as there’s enough raw data to work from.

 

 

 

 

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