Artificial Intelligence is a buzz word, a headline that gets thrown around as the future in so many ways. This article will look at what it really is and aimed at de-mystifying the two lettered acronym to something that can make sense to anyone.
So what is AI?? Artificial Intelligence is the use of algorithms to use in software to replicate something a human can do, and do it either the same or better. There are different types of AI general (AGI) and narrow (ANI). General is more what data scientists and futurists discuss when contemplating the future of humanity, for example the singularity, an artificial consciousness.
ANI refers to the more common usage of AI as it has (initially) one output eg text analysis or classification. This type of AI is most always what is in discussion when having real world applications and solutions.
So what can narrow AI offer and what are its impacts when applied in the real world?
Its important to define what the real world implications of each sub-sect of AI are (and there are many) as there are really endless applications that the tech can touch. It is often the case that a data scientist or AI consultant can never have worked on a industry or remotely similar problem statement but have a good appreciation of how to tackle the problem because of the unique way AI can be tweaked and deployed in such a wide range of environments, hence why the tech advances and moves so quickly over time.
The diagram below shows a very simple Venn diagram outlining the main topics that make up AI.
Machine learning is one of the most common examples of AI and explains software being used to retain a framework able to provide an output that was trained using past data. ie using past information give a useful output. This most often takes form as classification that can be adjusted to provided simple or complex answers to new data that is provided to the system.
Simply put, its aim is to learn and retain information.
One of the most powerful examples to date is when using machine learning in conduction with image recognition, where in 2012 the new systems were able to beat humans in that particular endeavour of AI.
Other uses include document analysis, language learning, computer vision, medical diagnostic, decision management, visual analysis, content classification, script generation and much much more.
The technology can really be applied to most cases where learning is required.
Language analysis and understanding is a multi layered capability of AI, in some ways it is very able to conquer understudying of the human language, able to provide fast outputs from text. In other ways much work is needed to provide full NLU or natural language understanding. NLU is more focused at taking the information from between the words ie context and sentiment.
The other side is NLP or natural language processing, which analyses the language and gives outputs not dependant on if the machine really ‘knows’ what is being said.
Once these systems are combined with state of the art speech to text transcriptions systems, (like most virtual assistants do) then audio can be processed and transferred into a wide array of useful outputs even to the point a human could never achieve. Hugely powerful diagnostic engines are a perfect example of how speech can be analysed by AI to provide great value.
Imagine you have to make a decision in the future or need to make an accurate decision on the spot, happens all the time, especially if you are dealing with customers or calculating next months sales demand. Decision management systems use information to program, this may be historical relevant data or logic to allow complex decisions to be programmed.
The benefits are many, the same consistent decision can be made with no external factors, the decisions can be scaled perfectly over many people with near zero extra cost. The decisions can be more accurate than human perception as no logical fallacies or bias can effect the decision. Some decision management systems are able to parse new information from big data to provide cutting edge insights that give huge commercial incentives.
Often maps of data can be displayed to show what is backing decisions, some systems rely on data feeds from the past eg the stock exchange or sales records or the weather to create state of the art model prediction.
Chatbots in this case includes virtual assistants and conversation bots that sit on websites. Conversational platforms at their simplest are programmed with logic to give certain answers at certain points, guiding the customer or personnel in the right direction. Eliminating the need for a long winded conversation to get to the point. At their most complex, an automated system factors many variables and past conversations and can provide human like feedback, most link to a server to process the data rich queries to then respond, like amazon Alexa.
The promise of chat based bots are that they can be augmented to suit a large range of scenarios. They can be trusted with data access as have no will to compromise the system so can be used to issue credentials and data where using humans would take time and wold need constant supervision. They can gap the initial interactions of people who need precise information, automatically pointing the user in the right direction or be handed to a person to further assist. Like many AI systems, the possibilities are endless.
It is clear we are all heading into ta digital future where interaction of one or many of these systems will become common place. Use of some systems could become essential to exist in the near future. It may be that considering recent events such as COVID19 that future is coming closer faster. Efficiency on a company level, large or small is more relevant than ever.
If any of the above could apply to you, why not see what is possible. Artificial Intelligence Companies such as Pro AI focus on providing relevant information to bridge the gap between what is possible.
Why not get in touch?