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Challenges of #ArtificialIntelligence

 

Until few years ago, #ArtificialIntelligence (#AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its initial stages. However now, Artificial intelligence (AI) is no longer the future. It is here and now. It’s realizing its potential in achieving man-like capabilities, so it’s the right time to ask: How can business leaders adapt AI to take advantage of the specific strengths of man and machine?

 

AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars, financial trading, connected houses etc. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers have leveraged massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. Therefore, the progress in robotics, self driving cars, speech processing, natural language understanding is quite impressive.

 

But with all the advantages AI can offer, there are still some challenges for the companies who wants to adapt #AI. As AI is a vast domain, lisitng all challenges is quite impossible, yet we’ve listed few generic challenges of Artificial Intelligence here below, such as: AI situated approach in the real-world; Learning process with human intervention; Access to other disciplines; Multitasking; Validation and certification of AI systems.

 

Artificial Intelligence’s Situated Approach:

Artificial Intelligence systems must operate and interact with the real world and their environment, receiving sensor data, determining the environment in which they operate, act on the real world, are such examples. Artificial Intelligence systems must behave autonomously and maintain their integrity under various conditions. To meet these requirements, AI systems must manage unstructured data as well as semantic data.

 

AL system and Human Intervention:

AI systems are programmed to interact with human users: they must therefore be able to explain their behavior, justify in a certain way the decisions they make so that human users can understand their actions and motivations. If this understanding is not forthcoming, human users will have little or no confidence in the AI’s systems, which isn’t acceptable. In addition to that, AI systems need some flexibility and adaptability in order to manage different users and different expectations. It is important to develop interaction mechanisms that promote good communication and interoperation between humans and AI systems.

 

AI, Opening to other disciplines:

An AI will often be integrated into a larger system of many other elements. Openness therefore means that AI scientists and developers will have to collaborate with specialists in other computer science disciplines (ex, modelling and predicting, verification and validation, networks, visualization, human-machine interaction, etc.) to compose a competitive and wider system of AI. The second aspect to consider is the impact of AI systems on many facets of our lives, our economy and our society, and therefore the collaboration with non-computer specialists such as psychologists, biologists, mathematicians, economists, environmentalists and lawyers is a must.

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Multitasking: 

Many AI systems are excellent-competent in a specific area, but turn out incompetent outside of their specific areas. However, systems operating in a real environment, such as robots, must be able to perform several parallel actions, such as memorizing facts, assimilating new concepts, acting on the real world and interacting with humans.

 

Validation and certification: 

The essential element of AI’s critical systems, the certification of AI systems or their validation by appropriate means, are real challenges, especially if they meet the expectations mentioned above (adaptation, multitasking, learning processes with human intervention). The verification, validation and certification of conventional systems (which are therefore not part of the AI) is already a difficult task – even if there are already exploitable technologies. The application of these tools to complex AI systems is a daunting task that needs to be addressed in order to be able to use these systems in environments such as airplanes, nuclear power plants, hospitals, and so on.

 

Other Generic Challenges: 

In addition to the previous challenges, the following requirements for AI systems should lead to new research activities: some are extremely complex and cannot be satisfied in the short term, but worth attention.
Implanting norms and values ​​into AI systems goes far beyond existing science and technology: for example, should a robot that will buy milk for its owner stop on the way to help a person whose life is in danger? Could a powerful AI technology be used by artificial terrorists? At present, AI research is far from being able to meet these requirements.
The privacy requirement is particularly important for AI systems confronted with personal data, such as intelligent assistants / companions or data mining systems. This requirement was already valid for conventional systems, but AI systems have the particularity that they will generate new knowledge from private data and will probably make them public if there are no technical means capable of imposing restrictions.

Final challenge concerns the scaling-up. AI systems must be able to manage large amounts of data and situations. We’ve seen learning algorithms that absorb millions of data points (signals, images, videos, etc.) and large-scale reasoning systems, such as the IBM Watson system, using encyclopedic knowledge. However, the question of scaling for the many V’s (variety, volume, speed, vocabularies, etc.) remains unanswered.

 

AI Applications: 

This is not strictly a challenge for AI, but it is important to highlight that AI systems contribute to resolve societal problems: AI applications cover the entire range of human activities, such as environment and energy, health and assisting living and home maintenance, transportation and smart cities, etc. They can be beneficial to mankind and the economy, but they can also pose threats if they are not controlled as planned.

How #DeepLearning is revolutionizing #ArtificialIntelligence

This learning technology, based on artificial neural networks, have completely turned upside down the field of artificial intelligence in less than five years. “It’s such a rapid revolution that we have gone from a somewhat obscure system to a system used by millions of people in just two years” confirms Yann Lecun, one of deep learning and artificial intelligence’s creator.

All major tech companies, such as Google, IBM, Microsoft, Facebook, Amazon, Adobe, Yandex and even Baidu, are using. This system of learning and classification, based on digital “artificial neural networks”, is used concurrently by Siri, Cortana and Google Now to understand the voice, to be able to learn to recognize faces.

 

What is “Deep Learning”?

 

In concrete terms, deep learning is a learning process of applying deep neural network technologies enabling a program to solve problems, for example, to recognize the content of an image or to understand spoken language – complex challenges on which the artificial intelligence community has profoundly worked on.

 

To understand deep learning, we must return to supervised learning, a common technique in AI, allowing the machines to learn. Basically, for a program to learn to recognize a car, for example, it is “fed” with tens of thousands of car images, labeled etc. A “training”, which may require hours or even days of work. Once trained, the program can recognize cars on new images. In addition to its implementation in the field of voice recognition with Siri, Cortana and Google Now, deep learning is primarily used to recognize the content of images. Google Maps uses it to decrypt text present in landscapes, such as street numbers. Facebook uses it to detect images that violate its terms of use, and to recognize and tag users in published photos – a feature not available in Europe. Researchers use it to classify galaxies.

 

Deep learning also uses supervised learning, but the internal architecture of the machine is different: it is a “network of neurons”, a virtual machine composed of thousands of units (Neurons) that perform simple small calculations. The particularity is that the results of the first layer of neurons will serve as input to the calculation of others. This functioning by “layers” is what makes this type of learning “profound”.

 

One of the deepest and most spectacular achievements of deep learning took place in 2012, when Google Brain, the deep learning project of the American firm, was able to “discover” the cat concept by itself. This time, learning was not supervised: in fact, the machine analyzed, for three days, ten million screen shots from YouTube, chosen randomly and, above all, unlabeled. And at the end of this training, the program had learned to detect heads of cats and human bodies – frequent forms in the analyzed images. “What is remarkable is that the system has discovered the concept of cat itself. Nobody ever told him it was a cat. This marked a turning point in machine learning, “said Andrew Ng, founder of the Google Brain project, in the Forbes magazine columns.

 

Why are we talking so much today?

 

The basic ideas of deep learning go back to the late 80s, with the birth of the first networks of neurons. Yet this method only comes to know its hour of glory since past few years. Why? For if the theory were already in place, the practice appeared only very recently. The power of today’s computers, combined with the mass of data now accessible, has multiplied the effectiveness of deep learning.

 

“By taking software that had written in the 1980s and running them on a modern computer, results are more interesting” says Andrew Ng. Forbes.

 

This field of technology is so advanced that experts now are capable of building more complex neural networks, and the development of unsupervised learning which gives a new dimension to deep learning. Experts confirms that the more they increase the number of layers, the more the networks of neurons learn complicated and abstract things that correspond more to the way of a human reasoning. For Yann Ollivier, deep learning will, in a timeframe of 5 to 10 years, become widespread in all decision-making electronics, as in cars or aircraft. He also thinks of the aid to diagnosis in medicine will be more powerful via some special networks of neurons. The robots will also soon, according to him, endowed with this artificial intelligence. “A robot could learn to do housework on its own, and that would be much better than robot vacuums, which are not so extraordinaire for him!

 

At Facebook, Yann LeCun wants to use deep learning “more systematically for the representation of information”, in short, to develop an AI capable of understanding the content of texts, photos and videos published by the surfers. He also dreams of being able to create a personal digital assistant with whom it would be possible to dialogue by voice.

 

The future of deep learning seems very bright, but Yann LeCun remains suspicious: “We are in a very enthusiastic phase, it is very exciting. But there are also many nonsense told, there are exaggerations. We hear that we will create intelligent machines in five years, that Terminator will eliminate the human race in ten years … There are also great hopes that some put in these methods, which may not be concretized”.

 

In recent months, several personalities, including Microsoft founder Bill Gates, British astrophysicist Stephen Hawking and Tesla CEO Elon Musk, expressed their concerns about the progress of artificial intelligence, potentially harmful. Yann LeCun is pragmatic, and recalls that the field of AI has often suffered from disproportionate expectations of it. He hopes that, this time, discipline will not be the victim of this “inflation of promises”.

 

Sources:

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