Artificial Intelligence or Cognitive Intelligence?

Friday, May 11, 2018

Artificial Intelligence or Cognitive Intelligence?

Content originally written in Spanish by Paloma Recuero de los Santos, LUCA Brand Awareness.

In recent years, the term “Artificial Intelligence” seems to have lost popularity in favor of other terms such as “Cognitive Intelligence”, “Smart Technologies” or “Predictive Technologies”. In this post, we return to the subject of a previous post and analyze why this trend has happened and look at the real difference between the terms.

A neurone network
Figure 1: Artificial Intelligence and Cognitive Intelligence are linked, but there are key differences.

    

What is AI?


The Encyclopædia Britannica defines the concept of Artificial Intelligence as the “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience”.

To summarize this rather length explanation in fewer words one could simply describe AI as creating a computer that can solve complex problems as a human would.

We may not all be aware of this fact, but Artificial Intelligence is a vital part of economic sectors such as Information Technologies, Health, Life Sciences, data analysis, digital transformation, security and even in the consumer sector with the development of smart homes etc.

Additionally, we will see that AI is closely linked to the four pillars of innovation and digital transformation: cloud computing, mobility, social analytics and Big Data. In the same way, AI is important for the main accelerators of this transformation; including cognitive systems, the Internet of Things (IoT), cybersecurity and more recently Big Data technologies.

But if it really is this important, why are people using the term artificial intelligence less and instead talking about cognitive intelligence, predictive technologies etc? Essentially, it’s due to the fact that Artificial Intelligence is weighed down by some negative connotations.

The origin of these negative connotations


Firstly, it seems that it has become a “worn-out” word. It has been used so widely that the whole world seems to know about (and have their opinion on) the subject! The same issues is facing the terms “cloud” and “internet of things”. This widespread use has been accompanied by a lack of information. Many people can only base their understanding on what Hollywood has taught them; that AI is limited to robots and Strong AIs. Others think they are talking about AI when in reality they are talking about Machine Learning. In reality, machine learning is one branch of artificial intelligence that allows researchers, data scientists, data engineers and analysts to build algorithms that learn and can make “data-driven” predictions. Instead of following a series of rules and instructions, these algorithms are trained to identify patterns in large quantities of data. Deep Learning takes this idea one step further and processes the information in layers, so that the result obtained in one later becomes the input for the next.

Secondly, there is the fact that artificial intelligence is not a new concept; in fact, it has existed since 1956. Over these years there have been different waves (such as the introduction of expert systems in the 80’s and the explosion of the internet in the 90’s). In each period, expectations have been greater than reality and there have been “troughs of disillusionment” the third phase of Gartner’s “Hype Cycle”.

Gartner Hype Cycle
Figure 2: The Gartner Hype Cycle. Source: Jeremykemp at English Wikipedia [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons

We currently find ourselves in a period of high expectations with respect to AI. In fact, Gartner believes that the three of the main technological trends of 2017 are different parts of AI (applied AI and Machine Learning, intelligent apps and the IoT). Therefore, Deep Learning (image detection, the creation of hypotheses etc) is immersed in our daily lives through different applications.

Some technological leaders talk about the dangers and impact that automation, robotics and AI might have on our lives and future jobs. Despite this, each day we are seeing more and more technologies that make our lives easier. However, as mentioned previously, the current tendency is to refer to intelligences as smart, cognitive or predictive instead of artificial. This may help to move away from the “baggage” of these negative connotations but they will always be there in the background.

What areas does AI encompass?


We have already seen that AI is a wide-reaching concept that touches on many different areas. In reality we could define it as an ecosystem, where we can include technologies such as data mining, natural language processing (NLP), Deep Learning, Predictive and Prescriptive Analysis and many more. In this ecosystem we also find recommendation systems that apps such as Uber and AirBnb base themselves on.

All of these technologies are characterized by generating data which, if analyzed correctly, can offer great value and understanding. Due to this, one can say that artificial intelligence lies at the convergence of all these solutions.

AI and digital transformations


The technology sector is transforming into a sector of understanding. In order to take anything away from this understanding it is important to have technologies and “real-life” applications that are deeply connected. This is what we call the “digital economy”. As mentioned earlier, this transformation is based on four fundamental pillars:

  • Cloud computing
  • Mobility
  • Social Analytics
  • Big Data Analytics

These technologies and innovations are the true driving forces behind a digital transformation and they are so closely tied with AI that they sometimes get confused with AI itself. These four pillars support the “accelerators” of innovation.

What are the accelerators of innovation?


The main accelerators are:

  • Cognitive services
  • Cybersecurity
  • IoT
  • Big Data

All of these things are ever-present in our daily lives. Cognitive services aim to imitate rational human processes. They analyze large amounts of data that is created by connected systems, and offer tools that have diagnostic, predictive and prescriptive capabilities that are capable of observing, learning and offering Insights. They are closely orientated with the contextual and human interaction. For this, the challenge for artificial intelligence is to design the technology so that people can interact with it naturally. This involves developing applications with “human behavior”, such as:

  • Listening and speaking, or rather the ability to turn audio to text and text to audio
  • Natural Language Processing (NLP). Text is not just a combination of keywords, a computer needs to understand grammatical and contextual connections too
  • Understanding emotions and feelings (“sentiment analysis”). To create empathetic systems capable of understanding the emotional state of a person and to make decisions based on this.
  • Image recognition. This consists of finding and identifying objects in an image or video sequence. It is a simple task for humans, but a real challenge for machines.

Cybersecurity is also moving towards a more holistic focus, one that considers its environment and a more human dimension. Above all, it is becoming more proactive. Rather than waiting for a cyber-attack to happen, the key is in prediction and prevention. Now, AI can be used to detect patterns in the data and take action when alerts arise.

What about the Internet of Things and Big Data? In this case, the amount of data that is being created is clear, as is the fact that it is happening rapidly and often in unstructured forms. This can include data from IoT sensors, social networks, text files, images, videos and sound. Now AI tools such as data mining, machine learning and NLP mean that it is possible to turn this data into useful information.


What is the difference between AI and Cognitive Intelligence?


After this brief analysis, we can now return to the initial question. Cognitive Intelligence is an important part of AI, that encompasses the technologies and tools that allow our apps, website and bots to see, hear, speak, understand and interpret a user’s needs in a natural way. That’s to say, they are the applications of AI that allow machines learn their users’ language so that the users don’t have to learn the language of machines. AI is a much wider concept that includes technology and innovations such as robotics, Machine Learning, Deep Learning, neural networks, NLP etc.

No comments:

Post a Comment