Demystifying Artificial Intelligence, Machine Learning and Deep Learning.
This Q&A format article has the objective to clarify artificial intelligence (AI) concept, focusing on providing a concise reference about what it is and where it is going, as soon as examples and reflections with a list of sources in the end, helping who needs to quickly know about AI and the issues and challenges around it.
What is Artificial Intelligence (AI)?
Artificial Intelligence or just AI, is considered the next big technological shift. It is defined as an information system that is inspired by a biological system designed to give computers the human-like abilities of hearing, seeing, reasoning, and learning. In current popular cases, AI refers to systems that change behaviors without being explicitly programmed, based on data collected, usage analysis, and other observations. AI is not one universal technology, rather it is an umbrella term that includes multiple technologies such as machine learning, deep learning, computer vision, and natural language processing (NLP) that, individually or in combination, add intelligence to applications.
So… AI is a new technology, right?
Actually not. Much of its theoretical and technological underpinning was developed over the past 70 years. Considered the first serious proposal in the philosophy of AI, Alan Turing published a landmark paper in 1950 where he devised his famous Turing Test, designed to be a rudimentary way of determining whether or not a computer counts as "intelligent." The term AI was coined by American computer scientist John McCarthy at the 1956 Dartmouth Conference, widely considered the birthplace of the discipline. Known as the father of AI, McCarthy created the Lisp computer language in 1958, which became the standard AI programming language and continues to be used today.
But why people and organizations are interested just now?
The accessibility of cloud computing, the ubiquitous availability of parallel processing power, near free data storage, and an exponential increase in data are enabling AI to truly have its day in the sun. AI capabilities that were in the realm of science fiction in the not-too-distant past are within reach of a broad range of enterprises, government, and private citizens.
Ok… give me a real example.
Here we go: https://www.youtube.com/watch?v=R2mC-NUAmMk
Why Microsoft decided to invest on AI?
According to Wired, Microsoft has “the resources, the data, the talent, and – most critically – the vision and culture to not only realize the spoils of AI, but also push the field forward.”
Check it out: https://www.wired.com/story/inside-microsofts-ai-comeback
More than that, Microsoft has been investing in the promise of AI for more than 25 years and has a bold vision: to build systems that have true AI across agents, applications, services, and infrastructure. This vision is also inclusive: democratize AI so that everyone can realize its benefits.
Some of the results of these efforts include Cortana, the “AI diva of the Microsoft empire” (according to Wired), a personal assistant that Microsoft considers an agent that can interact on a user’s behalf with other agents, and Bot Framework (https://dev.botframework.com/ ) and Cognitive Services (https://azure.microsoft.com/es-es/services/cognitive-services/?v=17.29 ), the set of tools and the 29 services like computer vision and voice recognition that Microsoft makes available to developers.
An interesting example of How Microsoft is Helping Conservationists Protect The Masai Giraffe: https://blogs.technet.microsoft.com/machinelearning/2016/04/11/how-microsoft-is-helping-conservationists-protect-the-masai-giraffe/
Also, Microsoft-owned Skype offers real-time language translations in six languages with more to come soon. As part of a federal crackdown on tech support scams, Microsoft's Digital Crimes Unit relied on AI to help track and take down the criminals behind them.
I though AI and Machine Learning was the same thing…
Actually, AI and machine learning are often used interchangeably but they are not the same thing and the misperception can cause confusion. Both encompass many different models, approaches, and implementations. Machine learning is the application, or “leading edge,” of AI based on the idea that machines get access to data and learn for themselves. Much of AI’s progress in recent years has been brought about by machine learning.
So, Machine learning is a type or sub-set of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. The idea has been around for almost 60 years, but big data and the need to make sense of it is what’s making the difference right now, along with great improvements in many AI disciplines such as neural networks and deep learning.
Since you have mentioned Deep Learning, what´s that?
Considered “undeniably powerful and transformative,” deep learning is a class or subset of machine learning algorithms that learns by using a large, many-layered collection of connected processes and exposing these processors to a vast set of examples. The design of this layered structure of algorithms, called artificial neural networks, is inspired by the biological neural network that the human brain uses. Although neural networks were first conceived in the 1950s, advances in algorithm design are currently taking place at the same time as rapid improvements in fast information storage capacity, high computing power, and parallelization.
Can you give me Business applications examples?
Sure, business applications of deep learning include textbased searches, fraud detection, handwriting recognition, image search, and translation. Amongst the broad range of problems lending themselves to deep learning are medical diagnosis, demand prediction, self-driving cars, customer churn, and failure prediction.
To give you an idea, internet giants and digital natives such as Amazon, Apple, Facebook, Google, and Uber already use AI technology building blocks.
Here you can see an example of how Uber is using it: https://www.youtube.com/watch?v=aEBi4OpXU4Q
Until now, very cool but… what about the other side of this story? What about the risks people use to be concerned eg: Job loses, Surpasses human intelligence etc?
This is probably AI’s biggest issue, and two predominant and contentious themes emerge about its risks. Some say progress in the field will jeopardize, even decimate, many jobs. A widely cited 2013 UK research paper estimated that roughly 47% of total US jobs are at risk of computerization or automation. Part of the answer will involve educating or retraining people in tasks AI tools aren’t good at, such as jobs involving creativity, planning, and “cross-domain” thinking – for example, the work of a trial lawyer. On the other hand, for example, Microsoft Cortana Intelligence and Machine Learning is helping Russell Reynolds Associates, a global leader in assessment, recruitment and succession planning to pairs ideal candidates with the right positions for them by tapping into more available data and by applying learning during the search process:
But it still a theme for the future when it comes to the effect of automation on jobs, in other words jobs will evolve rather than disappear. According to Forrester, by 2018, automation will change every job category by at least 25%. The bottom line can be summed up in one word: opportunity – as McKinsey & Co. puts it, “to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.” AI will increase the pace of change, which will require adaptation from people and from business.
And, whether it is science fiction or not, there are concerns about the “singularity,” that point in history when AI surpasses human intelligence, or whether instead of our controlling AI, it will control us. Although interesting to contemplate, this situation may not arise for hundreds of years, if ever.
Whether or not true AI is out there or is actually a threat to our existence, there's no stopping its evolution and its rise. Thanks to some notable successes, the only limit to the business problems AI is being asked to solve seems to be the human imagination.
What if I want to know more?
Check it out: https://www.microsoft.com/en-us/ai
Sources:
AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements PwC | Press release | June 27, 2017
See also: Sizing the prize, PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution, 2017
The Real Threat of Artificial Intelligence New York Times | Article | By Kai-Fu Lee | June 24, 2017
Inside Microsoft's AI Comeback Wired | Article | Jessi Hempel | June 21, 2017
Artificial intelligence is transforming enterprise software in a profound way AI and machine learning are bringing enterprise software developers and operations teams much, much closer to where the front-line customer action takes place. ZDNet | Article | By Joe McKendrick | June 14, 2017
Sorry humans, Microsoft’s AI is the first to reach a perfect Ms. Pac-Man score The Verge | Article | by Dani Deahl | Jun 14, 2017
How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017 Forbes | Article | Louis Columbus | Jun 11, 2017
How artificial intelligence can deliver real value to companies McKinsey Global Institute | Article | By Jacques Bughin, Eric Hazan, Sree Ramaswamy, Michael Chui, Tera Allas, Peter Dahlström, Nicolaus Henke, and Monica Trench | June 2017
See also: Artificial intelligence: The next digital frontier?, McKinsey Global Institute Discussion Paper, June 2017
Want to Know More About Machine Learning and AI? Customer Think | Article | Natalie Petouhoff, Ph.D. | May 31, 2017
8 Ways Machine Learning Is Improving Companies’ Work Processes Harvard Business Review | Article | Dan Wellers, Timo Elliott, Markus Noga | May 31, 2017
Tech giants acquired 34 AI startups in Q1 2017 VentureBeat | Article | May 28, 2017
10 tech giants investing in artificial intelligence: What is their plan and who are other key players? Techworld | Article | By Christina Mercer | May 22, 2017
Artificial Intelligence Software Revenue to Reach $59.8 Billion Worldwide by 2025 Advertising, Finance, Healthcare, Consumer, and Aerospace Are Some of the Sectors Leading AI Adoption, But Growth Will Be Strong Across Nearly Every Industry Tractica | Press release | May 2, 2017
Develop Your Artificial Intelligence Strategy Expecting These Three Trends to Shape Its Future Gartner (Access notes) | Report | Analyst(s): Whit Andrews, Gareth Herschel | Published: 20 April 2017
The Race For AI: Google, Twitter, Intel, Apple In A Rush To Grab Artificial Intelligence Startups CB Insights | Blog post | March 30, 2017
Top 10 Strategic Technology Trends for 2017: Artificial Intelligence and Advanced Machine Learning Gartner (Access notes) | Report | Analyst(s): Mike J. Walker, Alexander Linden, David W. Cearley | Published: 15 March 2017
Inside Facebook’s AI Machine Wired | Article | Steven Levy | 02.23.17
Artificial Intelligence Primer for 2017 Gartner (Access notes) | Report | Analyst(s): Whit Andrews, Alexander Linden, Tom Austin | Published: 03 February 2017
The 2016 AI Recap: Startups See Record High In Deals And Funding CB Insights | Blog post | January 19, 2017
TechRadar™: Artificial Intelligence Technologies, Q1 2017 AI Technologies Will Augment Your Enterprise Applications, Amplify Your Intelligence, And Unburden Your Employees Forrester (Access notes) | Report | By Rowan Curran, Brandon Purcell | January 18, 2017
10 Powerful Examples Of Artificial Intelligence In Use Today Forbes | Article | R.L. Adams | Jan 10, 2017
Five Things To Watch In AI And Machine Learning In 2017 Forbes | Article | Karl Freund, Moor Insights and Strategy | Jan 6, 2017
The Fourth Transformation: Augmented Reality & Artificial Intelligence Forbes | Article | John Koetsier | Dec 12, 2016 .
Parabéns pelo artigo meu querido amigo Frederico De Marchi. Very well done!
Thanks a lot Murilo da Costa
Great summary Frederico De Marchi.