Approaching the topic of Artificial Intelligence nowadays almost borders on the trivial. Why? Because due to AI being such a trending technology, the vast majority of tech-related professionals hitch their wagon to its star.
Therefore it may actually be difficult to discern between what really matters in AI development and what is just there for the buzz. Clouds of confusion surround the most currently hyped sector of technology, seeing how the large public doesn’t know enough specific details yet. Nevertheless, there are available reliable sources that help those interested in having the right idea on what is going on, and what really matters.
Let’s see if we can use these to review the most important AI moments in the year that just went by.
What is the actual timeline with AI?
Artificial Intelligence learns in order to evolve. That is why the term entangles itself with another specialty notion – machine learning. Machine learning, in turn, features various sub-branches, such as Deep Learning (DL) and Reinforcement Learning (RL). There are rather mysterious for those who don’t work with algorithms.
The theory applied in this field of technology preceded the actual physical (and software) tools by decades. What suddenly made it possible for the theories to materialize is the accessibility of big data. Today the professionals benefit from the hardware and software capable to actually support what AI represents in terms of data. Microsoft pushed Open AI out into the open, Google did the same with DeepMind due to being able to feed massive data sets to their machines.
While the important players in the field deal with what is means to see their theoretical concepts brought to life, there are a lot of fake AI ventures that raise the risks for those willing to invest.
In what the future holds, the companies don’t seem eager to publicly broadcast their internal deadlines. The industry events allow a few sneak peaks into what they have developed and prototype-d lately. Yet the stakes are high and the competition tight, so there is a lot of secrecy involved. This means we might be taken by surprise, when high-tech products appear on the market.
Do smaller companies have access to authentic AI ventures?
While the more resourceful and powerful companies took their older theoretical projects into the testing phase, smaller businesses dealing in AI fall into two categories. They may be either bright ventures that approach the matter from an innovative angle, either bogus endeavors.
Where could a small-sized company find the technical means to make development possible? We have now various funding platforms that serve precisely for this. Plus, the big companies often open source their databases to the research community, as Google did last year with various project databases.
Company size is irrelevant in AI development, as long as professional collaboration may extend resources’ availability. Surely, depending on the project, there are ideas and theories that necessitate supercomputers. We have but a few of these in the entire world, therefore such projects depend on getting in line and actually benefiting from the outstanding opportunity of next-gen computers.
What are the voices in the Artificial Intelligence chorus?
Furthermore, there are a couple of parallel directions in the evolution of machine learning.
In the more academic laboratories, decades of research come to fruition with the help of modern technology. AI probably looks totally unattractive there – a bunch of boxes, cables, screens and various tests that would mean nothing to the average man. Nothing like the movies, no talking or walking robots, serving the researchers with coffee.
Meanwhile, the market features Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana or Google Assistant. Coming in various forms, in sleek devices, these ML illustrations familiarize people with what AI represents in real life. IBM’s Watson is also functional, but it does not come in an attractive design. IN fact the large public is not its target at all. Watson helps researchers and specialists deal with vast projects. It also provides hope for the cyber-security community in fighting the increasing number of cyber incidents.
Another side of Artificial Intelligence consists of enterprise robotics. These machines are nor pretty nor necessarily tiny. Yet their capabilities are comprehensive. Depending on the industrial field they are destined for, the industrial robots of our times also went above their previous technological limits. One of the novelty here is their networking ability. Having a team of robots communicate among themselves improves the production flux and speeds up the operational pace.
What can AI enthusiasts actually buy now?
I imagine that those who are passionate about smart machines could feel a certain disappointment. The necessary theories exist for decades. Various publications featured utopia visions of the post 2000 future of humanity – filled with robots. Lifelike cinematic productions showed us how would it be like to live with advanced robotics in our everyday lives. Yet – what do we have? What are the available AI devices and how much do they cost?
Here’s a selection put together by Popular Mechanics. It features a few of the best robots at affordable prices. They borderline the toys’ department, yet these are backed up by research and science. Adults can enjoy them as well. Featuring Orbotix Ollie or WowWee MiP, the list includes robots that basically do almost nothing, but they are functional and fun.
Another list from NetworkWorld, in fact a video, tries to sum up the year 2016 in what robotics is concerned. Boston Dynamic’s SpotMini, the Zenbo personal assistant robo or Laundroid gained their place in this cool robots’ top.
We leave you with a list of NY Times titles filed under Artificial Intelligence – it should make you think of exciting ML-related future possibilities. However, there are also concerns linked to humanity’s AI-infused future. You pick the most pressing out of the two.