Artificial Intelligence is cool, period. Envisioned as a tool giving computers a ‘consciousness’ of sorts, in books and media since time immemorial, AI can be heralded as one of the biggest technological advancements in recent times. And when AI meets Context, the results and outcome can only become bigger and better.
Recent trends in the arena of Machine Learning have paved the way for advancements in Artificial Intelligence, and with the advent of Context and Contextual Applications for various platforms, the field of Artificial Intelligence just keeps getting better and better. What exactly are the advancements? How are they viable today? And how are big corps making the most out of this development. In this article, I talk about how a Contextual approach in Artificial Intelligence can actually prove to be a trump card in Technology.
EASING INTO THE BASICS
I’ve already spoken a lot about Context in my previous articles. The ability to extend intelligence beyond the human brain into computer hardware and software alike is turning out to be the game changer in recent times as far as product design is concerned.
The meaning of Artificial Intelligence can be quite easily implied from the term itself, but the scope of Artificial Intelligence is quite vivid. Deep Learning and Neural Networks are composed of AI, and it can be applied to a lot of things, not merely studying user inputs. AI is finding its scope in Data Mining and Machine Learning too, and all this ultimately proves how important AI can become in the future.
COMPONENTS OF ARTIFICIAL INTELLIGENCE
A simple term by itself, Artificial Intelligence actually incorporates a lot more concepts, as demonstrated in this diagram.
We’ve already spoken about Machine Learning previously, and the concept of Artificial Intelligence is also evident in recent developments such as Viv. All in all, the field of AI is truly up and coming!
Artificial Intelligence basically works on a four plane model which emulates the working of the human brain (yep, the concept is akin to that of a neural network!)
Programmers and developers will agree with me when I say that the underlying force of Context uses a simple ‘if’ condition, and the ‘if’ condition is present in these four planes as well. The results of each plane forms the foundation for the next plane, and so on till the desired output is achieved.
• Concept Plane : The program receives inputs in the form of data from sensors (sensors are very important, as we’ve spoken about here), or inputs from the user.
• Pattern Plane : Step two of the equation, where the ‘artificial intelligence’ senses similarities in pattern between what has been received as the input and what conditions are stored within the system.
• Prime Plane : This is where things get interesting. Here, the software makes a decision as to what needs to be done regarding the particular input.
• Action Plane : Once the decision is taken by the software, the relevant action is performed.
Context plays a defining role in the equation, since it governs the Pattern and Prime planes. The intelligence in AI, well that comes from Context!
One thing was certain from the developments in the last few years – Social Networking is in. The various trends in the social networking sector all hint at the paradigm shift in consumer (and developer) interest in this field, and more advancements will prove to be game changing in this regard.
Facebook’s AI team is building a tool which can help users retrieve memories in the form of Artificial Intelligence, Image Processing and complex computing. The data is received from representations of model based alignment and the very large facial database contained within Facebook, and as a result the DeepFace project promises an image recognition accuracy of 97.25%.
Google’s biggest USP is undoubtedly, their search engine. And their technology utilises a lot of isolated AI mechanics which work in unison to enhance and support the search engine. The key driving force in this regard is the element of intuition, and Google is developing complex algorithms to incorporate AI and improve the quality of results.
A simple example : Google Translate uses quite a few elements of machine learning. Initially, a lot of translated documents and books were fed to Google as samples, and when a user wants to translate something, Google Translate cross references the number of documents in its database and reconstructs the word/phrase in another language as accurately as possible.
Also, I believe that to get a keen understanding of a company’s direction, a lot can be gathered from the mergers and acquisitions that it participates in. Early in 2014, Google brought DeepMind, a London based AI company which specialises in games and eCommerce algorithms. Larry Page made Google’s plans clear in a TED conference, when he said that the company was in an effort to build the world’s best personal assistant – which could answer queries even before the users asked.
And what better way to do this, than through using Context?
COGNITIVE COMPUTING BY IBM
IBM’s Watson is basically a supercomputer with an element of cognitive computing – which makes sense of a plethora of unstructured information to deliver simple and concise results. The real icing on the cake in this case is the availability of the AI through API releases to developers by Watson’s team, and this will in turn encourage more developers to make full use of Context, cognitive computing and AI.
PERSONAL ASSISTANTS GALORE
Finally, the most apt examples to demonstrate how far AI has come through the years are the personal assistants. I’ve already spoken about how Siri is becoming more intelligent, and with the wide gamut of developments that are underway in Microsoft’s Cortana and Google Now, personal assistants, aided with Context and Artificial Intelligence, can truly make our phone and devices smarter.
Context and Artificial Intelligence, aided with the growth of the Sensor market and relevant hardware and software applications, will indeed be the game changer in the near future!