Today artificial intelligence (AI) is used in healthcare (diagnostics), automotive (self driving cars), finance and economics (financial trading), video games (non player characters), military (lethal autonomous weapons), advertising (personalised promotions) and, not to forget: Art (Thinking Machines: Art and Design in the Computer Age, 1959–1989).
To get a better picture of artificial intelligence it helps to look at the term intelligence. As feared, the definition of intelligence is controversial. A group of researchers tried to find one in the year 1994 and published a public statement called “Mainstream Science on Intelligence” with 25 conclusions. This definition seems to be a good summary.
Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—”catching on,” “making sense” of things, or “figuring out” what to do.
Now add “artificial” to this definition and you might have a better idea. It’s about intelligent machines. When it now comes to the possibilities and possible perils of artificial intelligence there are lots of opinions out there. From the end of the world to the ultimate bright future everything is possible.
Let’s have a look at a few quotes:
“The development of full artificial intelligence could spell the end of the human race. It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”
Stephen Hawking – Stephen Hawking warns artificial intelligence could end mankind
“Artificial intelligence would be the ultimate version of Google. So we have the ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. That’s obviously artificial intelligence, to be able to answer any question, basically, because almost everything is on the Web, right? We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on. And that’s tremendously interesting from an intellectual standpoint.”
Larry Page – Interview, Oct. 28, 2000
“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most.”
Elon Musk – in a comment on The Myth of AI
“I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though, the intelligence is strong enough to be a concern.”
Bill Gates – Microsoft’s Bill Gates insists AI is a threat
“I have pretty strong opinions on this. I am optimistic. I think you can build things and the world gets better. But with AI especially, I am really optimistic. And I think people who are naysayers and try to drum up these doomsday scenarios — I just, I don’t understand it. It’s really negative and in some ways I actually think it is pretty irresponsible”
We stand at the birth of a new millennium, ready to unlock the mysteries of space, to free the Earth from the miseries of disease, and to harness the energies, industries and technologies of tomorrow.
“Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world. If we become leaders in this area, we will share this know-how with the entire world, the same way we share our nuclear technologies today”
“The State Council paper laid out China’s desire to be a hub of AI innovation by 2030, and these papers have teeth in terms of very strong local execution. There are a huge number of engineering students who are ready to go into AI. A lot of people misunderstand AI as a brilliant scientist invents another AI algorithm for medicine, finance, loans, banking, autonomous vehicle, face recognition… But that is just not the way AI business is run. There is really one fundamental AI innovation – deep learning – and everybody else is tweaking it for the domains. So, we’re not in the age of discovery; we’re in the age of implementation, we’re in the age of data, and China has a better set, a larger set of implementers or good AI engineers who get the work done, who make the algorithms run fast, connect to business logic”
[…] attempting to distil intelligence into an algorithmic construct may prove to be the best path to understanding some of the enduring mysteries of our minds.
Demis Hassabis – Deepmind (2012) – Is the brain a good model for machine intelligence?
So basically it still seems a bit unclear and sometimes contra-dictionary what AI is about.
But so far everyone understood that AI will change a lot.
In most business related cases people are talking about machine learning instead of artificial intelligence. Their main goal is to analyse data in a better way. The idea behind is easy. The more data you have, the more “intelligence” is somewhere inside. Kai-Fu Lee seems to be right, when he says, it’s all about the amount of data, the AI algorithms and the connection to business logic.
Clive Owen summarises especially the connection to business logic in a nice way for SAP.
For companies the outcome is important and there must be a way to use all this data to actually “do” something.
But how does it work?
Usually when a programmer writes code, they code is kind of static. The program is only able to execute the different commands. If the program has to work differently then the programmer has to change some commands. The programmer is in full control of the outcome of the program.
Now imagine a robot that is able to walk. It goes somewhere and there is a tree. How does it learn to go around that tree? The program that’s inside the robot must be somehow different. It needs the ability to learn on the fly and to change it’s behaviour when necessary. This different programming approach are called artificial neural networks. The programmer create a foundation (the neural network) and the program itself learns by creating weighted relations.
The problem is that the programmer can only provide the foundation. The next big step is to apply these neural networks to the tasks you wish to optimise. They have to learn! Raia Hadsell from the AI company DeepMind (now owned by Google) describes it in an understandable way using Atari games.
To solve a complex problem with AI it has to be divided into smaller pieces. Each smaller piece is easier to understand. And if the algorithm knows the way to solve some tasks, the next step is to combine them and go for the whole problem. If it’s possible for algorithm to solve the complex problem there should be a way to remember the decisions taken otherwise it has to learn again when the problem occurs next time.
Now imagine these type of algorithms in a business environment. When you think of the automated logistics of a warehouse or a container terminal it’s simply awesome.
The hard thing is the learning phase in the beginning and finding the best algorithms (better not with linear algebra 😉 )
Even if you not agree to Mark Zuckerbergs quote above in general, it is not hard to predict that the combination of your business data and AI algorithms makes a lot of sense and probably the world will survive.
There are already plenty of examples for intelligent products and services using machine learning algorithms. I want to mention a few to get an idea how common AI already is.
- Speech recognition: Apple Siri, Google Assistent, Microsoft Cortana, Amazon Alexa, Samsung Bixby
- Consumer goods: The “invention” of Cherry Sprite, the Roomba vacuum cleaner and the AI enabled Barbie.
- Financial data: Experian Credit Score, Sentient Investment Management
- Retail: “Google analytics for the real world” – shopperception.com
- Healthcare: Infervision helps radiologists
- Manufactoring: Every car manufacturer is working on self-driving cars on level 5 (fully automated): Who’s Winning the Self-Driving Car Race?
- Projects: Global Fishing Watch
How can my company integrate machine learning?
Every company should know how machine learning can optimize existing solutions. You don’t have to be an expert in machine learning to use every intelligent application. Machine learning is already integrated into SAP products such as Concur and SAP S/4HANA and, using the SAP Leonardo Machine Learning Foundation, every SAP partner or SAP user can connect and implement readily prepared services, and use their own models to create intelligent applications.
SAP Cloud Platform is the foundation for the development and distribution of all types of intelligent application, and for high-performance operation. As a result, machine learning technology is available and many companies have found approaches to use it effectively (read more in the SAP News Center: What is Artificial Intelligence?).
These types of algorithms transform your business into an intelligent enterprise that uses all it’s data.