With eyes towards the future, it would seem that all of life’s conceivable problems could be solved through artificial intelligence, machine learning, and algorithms.
Machine learning – the subset of AI where machines are able to learn from data without the need to be programmed and algorithms, ordering data based on a pre-set formula – are the best examples of AI we have.
We already use algorithms prolifically – from Netflix and Spotify to the way we use our social media, but machine learning takes things to a new level, relying on not just the inputted formula, but on real-time data. How could the machine learning of today inform the machine learning of the future?
Machine Learning at Work
There are many ways that machine learning, algorithms, and broader AI have been used to enhance our lives so far – which shows promise for the future of the industry and the solutions it can provide.
For instance, we benefit from the improvements AI offers every day when we open our smartphones. Artificial intelligence has already been deployed to improve efficiency in mobile app development as it can help enhance devOps and increase the speed of automation. But what else do we use the forms of AI for?
Most websites offer a live chat experience – and a lot of this comes from automated chatbots before the customer service team steps in. Constant development means that the chatbots learn and improve through the input presented to them.
Two of the three primary requirements of a chatbot that uses natural language processing (communication between man and machine) are usually fulfilled.
They can provide an answer to the question (what time does the business open? do they stock a certain product?) and they understand the context of the conversation. The final requirement would be to come across as a human and not a bot, which is still yet to be accomplished.
Forex trading is another field in which some form of algorithmic machine learning has been implemented. Finding suitable automated forex trading robots involves conducting backtesting, which looks at how well the robots have performed historically, as well as analyzing real-time live results to ensure that the robots are able to conduct adequate trades for the account holder.
The information is presented to the robot based on how currencies have performed previously, and how they might perform in the future based on a series of conditions.
Even qualitative information on a political or economical scale can be measured with related quantitative metrics, which help the robots provide accurate information. The future of forex bots would be to help better analyze qualitative data.
Perhaps the most recognizable form of this new technology in our day-to-day life is with our virtual assistants – Google Home, Amazon Echo, and Apple’s Siri. Your commands are responded to – questions are searched online, other app data is consulted if necessary for things like schedule and messages, and the assistant usually understands the context of your request.
Machine learning comes into play to personalize the service as your previous settings and requests are stored as reference data to create more specific responses in future.
The more people who use virtual assistants, the better the technology will be to create ones with greater instincts, able to complete more commands with fewer distinct contextual cues. Every virtual assistant owner will know that there are certain commands that are more of a struggle – and it’s these kinks that need ironing out.
As with the (wrongly predicted) flying cars that used to symbolize the future, the automotive industry has remained the benchmark for intuitive tech. Self-driving cars are often lauded as the next step in our development of using artificial intelligence and machine learning.
With some already on the road, many drivers are skeptical based on negative stories. But, machine learning for cars doesn’t just feed into driverless or self-driving vehicles.
Volvo, for example, collects data to inform which parts of the car may fail more often and uses the network to develop best practices when it comes to driver safety.
They have developed garbage trucks that work using this principle. BMW are paving the way with driverless technology based on the data they have already collected and touted the fact they may be read for fully human intervention-free driving by 2021. Safety-first car companies may have more cache with consumers when selling their driverless cars, especially with such a wealth of data to ensure maximum accuracy.
The future of machine learning won’t happen as suddenly as it is depicted in fiction – with fully intuitive robots helping around the house. Instead, we will see a greater proliferation of machine functions that work more instinctively than they used to, and which save us a lot of time.
Building on from the algorithms that work to a set of parameters, machine learning will improve the way it interacts with stimuli in order to provide us with faster responses. The key facet of the future of AI through machine learning and by building on algorithms will be utilizing qualitative data as well as quantitative data.