Do we really need "AI"d?
So, how did all of this start? and how did It progress?
The term artificial intelligence was coined in 1956, Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names. Not long after that, the term itself became strongly related to the term "Machine learning" which is a branch of AI that is based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Nowadays the term AI is strongly related with the term "Deep learning" which is a branch of "Machine learning" that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions.
Mind blown by complicated definitions? We have not even defined AI just yet!
To simplify, Artificial Intelligence is about making machines smarter, so that they can think and act like humans (or even better). AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities.
When we talk about branches of AI, we find ourselves talking about how AI works and how it's integrated in today's technologies. AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major sub-fields:
- Machine learning is concerned about finding and collecting information though neural networks and efficient algorithms.
- Deep learning uses huge neural networks to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
- Cognitive computing is a sub-field of AI that aims for an ultimate goal that is for a machine to simulate human processes through the ability to interpret images and speech – and then speak coherently in response.
- Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video.
- Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech.
To sum this up, I am going to present to you a deep example of an AI tool that's becoming available on smartphones we use everyday.
While this app can already perform functions such as open applications, search for nearby locations, and respond to search queries, Google demonstrated what else it could possibly do in the future.
At the I/O Developer’s conference on May 8, 2018, Google CEO Sundar Pichai presented some of Google Assistant’s upcoming features. One of the most notable features is the ability to make a phone call, which was met with awe from the audience.
Another update is the integration of Google Assistant into Google Maps, which will help users find nearby places, such as restaurants and business establishments. Users will also have a personalized set of recommendations based on their preference and review scores. The popular service will also be integrating Google Lens, which would help users look for important places using images and Street View, and navigate much better. This feature can also be used to integrate augmented reality as well, which can be used by local businesses to tap into possible customers.
References:
Keep up a good work! Thanks
ReplyDelete