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COGNITIVE COMPUTING: WHEN MACHINES THINK LIKE MEN

Techsol S.r.l.
Jul 27, 2017 · 3 min read

The digital world we live in today is based on a constantly interconnected technology, capable of producing a growing amount of data. Every day we produce 2.5 exabytes, about 2 billion and a half USB 1 Giga keys, and most of this data was created only in recent years, worldwide.
This incredible amount of information is a consequence of the digitalization process that characterizes our era; some studies estimate that their total volume will reach 35,000 exabytes in 2020. This is the phenomenon we now all know as Big Data.
By managing such a quantity of data and extracting value, machines must become more and more intelligent. They must be able to read the data in any available format (text, audio, images, video, static or dynamic) and in any available database (from classic to social networks).

The use of cognitive technologies can help companies in their daily lives, as a tool to exploit to make the organization more efficient, improve productivity and reduce costs. The importance of this market is also highlighted by the type of investment planned for the coming years. By the end of 2018 at least 75% of companies will have implemented some cognitive computing functions within at least one business application. By 2019, 40% of the digital transformation projects — according to Industry 4.0 parameters — will be supported by these technologies. Index of a strongly active market on this issue.
The main features of cognitive computing are: adaptability (it is able to modify reasoning based on variability of incentives) and obviously interactivity (thanks to natural language mechanisms that allow CC systems to relate to humans) . At the heart of this phenomenon there are softcomputing models that allow you to evaluate, control and calculate in an imprecise scope using the ability of humans to perform certain activities based on their experience. These paradigms have led to deep learning technology, which brings to light those deep-learning architectures, such as deep neural networks, which have been applied in computer vision, in automatic recognition of speech, in the elaboration of natural language, in audio recognition and bioinformatics.
Numerous companies have been interested in Cognitive Computing technology, first IBM with the Watson system, but also Microsoft (Cognitive Computers), HPE (Haven OnDemand), Cisco (Cognitive Threat Analytics), Google (DeepMind), Cognitive Scale, Customer Matrix (Cognitive Engine), SparkCognition (Spark Prediction). Each of these realities pursues a development strategy with the aim of making available mechanisms that allow the developer to create applications with automatic learning and reasoning features.

How can this tool help your business?
Andrea Peyracchia, Partner Digital McKinsey, said that we need to focus on 3 points: internal consensus, talents and technology.
Consensus is needed by top management, who must realize the need to develop concrete projects from cognitive computing. Talents must be cultivated, specifically the data scientists who plan artificial intelligence, the data engineers who deal with all these data, and the translators, which know about business and artificial intelligence, and thus represent the union element of all involved parties. Finally, technology, mainly based on cloud and open source, cost-cutting tools.

In conclusion we can say that the capabilities of cognitive computing are available to many business applications, from customer experience to supply chain, from collaboration to human resources, etc.. These technologies can serve the introduction of self-service logic in various business areas, going beyond the IT automation needs and demonstrating their real potential in core business management.

Techsol S.r.l.
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