Artificial Intelligence for Improving Data Processing

Within this framework, five leading scientists presented the latest advances in their research work on different aspects of AI. The speakers tackled issues ranging from the more theoretical such as algorithms capable of solving combinatorial problems to robots that can reason about emotions, systems that use vision to monitor activities, and automated players that learn how to win in a given situation."Inviting speakers from groups of references allows us to offer a panoramic view of the main problems and the techniques open in the area, including advances in video and multi-sensor systems, task planning, automated learning, games, and artificial consciousness or reasoning," the experts noted.

The participants from the AVIRES (The Artificial Vision and Real Time Systems) research group at the University of Udine gave a seminar on the introduction of data fusion techniques and distributed artificial vision. In particular, they dealt with automated surveillance systems with visual sensor networks, from basic techniques for image processing and object recognition to Bayesian reasoning for understanding activities and automated learning and data fusion to make high performance system. Dr.Simon Lucas, professor at the Essex University and editor in chief of IEEE Transactions on Computational Intelligence and AI in Games and a researcher focusing on the application of AI techniques on games, presented the latest trends in generation algorithms for game strategies. During his presentation, he pointed out the strength of UC3M in this area, citing its victory in two of the competitions held at the international level during the most recent edition of the Conference on Computational Intelligence and Games.

In addition, Enrico Giunchiglia, professor at the University of Genoa and former president of the Council of the International Conference on Automated Planning and Scheduling (ICAPS), described the most recent work in the area of logic satisfaction, which is rapidly growing due to its applications in circuit design and in task planning

Artificial Intelligence (IA) is as old as computer science and has generated ideas, techniques and applications that permit it to solve difficult problems. The field is very active and offers solutions to very diverse sectors. The number of industrial applications that have an AI technique is very high, and from the scientific point of view, there are many specialized journals and congresses. Furthermore, new lines of research are constantly being open and there is a still great room for improvement in knowledge transfer between researchers and industry. These are some of the main ideas gathered at the 4th International Seminar on New Issues on Artificial Intelligence), organized by the SCALAB group in the UC3M Computer Engineering Department at the Leganés campus of this Madrid university.

The future of Artificial Intelligence

This seminar also included a talk on the promising future of AI."The tremendous surge in the number of devices capable of capturing and processing information, together with the growth of the computing capacity and the advances in algorithms enormously boost the possibilities for practical application," the researchers from the SCALAB group pointed out. Among them we can cite the construction of computer programs that make life easier, which take decisions in complex environments or which allow problems to be solved in environments which are difficult to access for people," he noted. From the point of view of these research trends, more and more emphasis is being placed on developing systems capable of learning and demonstrating intelligent behavior without being tied to replicating a human model.

AI will allow advances in the development of systems capable of automatically understanding a situation and its context with the use of sensor data and information systems as well as establishing plans of action, from support applications to decision making within dynamic situations. According to the researchers, this is due to the rapid advances and the availability of sensor technology which provides a continuous flow of data about the environment, information that must be dealt with appropriately in a node of data fusion and information. Likewise, the development of sophisticated techniques for task planning allow plans of action to be composed, executed, checked for correct execution, and rectified in case of some failure, and finally to learn from mistakes made.

This technology has allowed a wide range of applications such as integrated systems for surveillance, monitoring and detecting anomalies, activity recognition, teleassistence systems, transport logistic planning, etc. According to Antonio Chella, Full Professor at the University of Palermo and expert in Artificial Consciousness, the future of AI will imply discovering a new meaning of the word"intelligence." Until now, it has been equated with automated reasoning in software systems, but in the future AI will tackle more daring concepts such as the incarnation of intelligence in robots, as well as emotions, and above all consciousness.

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Mapping the Brain: New Technique Poised to Untangle the Complexity of the Brain

A new area of research is emerging in the neuroscience known as 'connectomics'. With parallels to genomics, which maps the our genetic make-up, connectomics aims to map the brain's connections (known as 'synapses'). By mapping these connections -- and hence how information flows through the circuits of the brain -- scientists hope to understand how perceptions, sensations and thoughts are generated in the brain and how these functions go wrong in diseases such as Alzheimer's disease, schizophrenia and stroke.

Mapping the brain's connections is no trivial task, however: there are estimated to be one hundred billion nerve cells ('neurons') in the brain, each connected to thousands of other nerve cells -- making an estimated 150 trillion synapses. Dr Tom Mrsic-Flogel, a Wellcome Trust Research Career Development Fellow at UCL (University College London), has been leading a team of researchers trying to make sense of this complexity.

"How do we figure out how the brain's neural circuitry works?" he asks."We first need to understand the function of each neuron and find out to which other brain cells it connects. If we can find a way of mapping the connections between nerve cells of certain functions, we will then be in a position to begin developing a computer model to explain how the complex dynamics of neural networks generate thoughts, sensations and movements."

Nerve cells in different areas of the brain perform different functions. Dr Mrsic-Flogel and colleagues focus on the visual cortex, which processes information from the eye. For example, some neurons in this part of the brain specialise in detecting the edges in images; some will activate upon detection of a horizontal edge, others by a vertical edge. Higher up in visual hierarchy, some neurons respond to more complex visual features such as faces: lesions to this area of the brain can prevent people from being able to recognise faces, even though they can recognise individual features such as eyes and the nose, as was famously described in the book The Man Who Mistook Wife for a Hat by Oliver Sachs.

In a study published online April 10 in the journalNature, the team at UCL describe a technique developed in mice which enables them to combine information about the function of neurons together with details of their synaptic connections.

The researchers looked into the visual cortex of the mouse brain, which contains thousands of neurons and millions of different connections. Using high resolution imaging, they were able to detect which of these neurons responded to a particular stimulus, for example a horizontal edge.

Taking a slice of the same tissue, the researchers then applied small currents to a subset of neurons in turn to see which other neurons responded -- and hence which of these were synaptically connected. By repeating this technique many times, the researchers were able to trace the function and connectivity of hundreds of nerve cells in visual cortex.

The study has resolved the debate about whether local connections between neurons are random -- in other words, whether nerve cells connect sporadically, independent of function -- or whether they are ordered, for example constrained by the properties of the neuron in terms of how it responds to particular stimuli. The researchers showed that neurons which responded very similarly to visual stimuli, such as those which respond to edges of the same orientation, tend to connect to each other much more than those that prefer different orientations.

Using this technique, the researchers hope to begin generating a wiring diagram of a brain area with a particular behavioural function, such as the visual cortex. This knowledge is important for understanding the repertoire of computations carried out by neurons embedded in these highly complex circuits. The technique should also help reveal the functional circuit wiring of regions that underpin touch, hearing and movement.

"We are beginning to untangle the complexity of the brain," says Dr Mrsic-Flogel."Once we understand the function and connectivity of nerve cells spanning different layers of the brain, we can begin to develop a computer simulation of how this remarkable organ works. But it will take many years of concerted efforts amongst scientists and massive computer processing power before it can be realised."

The research was supported by the Wellcome Trust, the European Research Council, the European Molecular Biology Organisation, the Medical Research Council, the Overseas Research Students Award Scheme and UCL.

"The brain is an immensely complex organ and understanding its inner workings is one of science's ultimate goals," says Dr John Williams, Head of Neuroscience and Mental Health at the Wellcome Trust."This important study presents neuroscientists with one of the key tools that will help them begin to navigate and survey the landscape of the brain."

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