We are so used to saying that Artificial Intelligence is a software development trend that we end up ignoring an important detail. AI is not just a technology but a complex structure of available methods and algorithms that include Machine Learning, Natural Language Processing and so on. And what about new Artificial Intelligence trends?
AI might be a trend itself but there are domains that develop most actively in that complex field of Artificial Intelligence. What determines how Artificial Intelligence will look tomorrow and what business should already start investing in?
These are 5 reasons why AI development will not be the same next year:
- AI Cloud
- Homegrown AI Languages
- AI Chipsets
- AI Marketplaces
- Cognitive Computing
Reason #1 – AI Cloud
This is a smart combination of two of the hottest fields of modern technology – Artificial Intelligence and Cloud. Powered by AI, storages can analyze data, learn, and understand advanced search queries. This means a lot for information management, especially for enterprise development solutions, where tons of datasets are processed every day.
Let’s say, you want to find an image on Google Drive but forgot how you named it. If you ask an algorithm to find ‘a pic of forests and mountains’, a neural network will identify it and show the file. The combination of AI and Cloud is the first step to smart storages that could be able to process and understand the stored information.
In 2017, big corporations have raced to capture AI-cloud-share and become a trusted provider of AI remote services. IBM, Amazon Web Services, Microsoft’s Azure, IBM, and Alphabet are all investing in engaging AI developers to store their code in the Cloud which will be further used for the companies’ innovations.
AI Cloud allows businesses to store, share, and exchange Artificial Intelligence. That was proven by Amazon’s SageMaker (a service where developers create and train their own neural nets) and Microsoft’s Azure platform where developers work with AI-toolkits, store the code in Cloud, and can run it on connected devices—right within the platform!
Reason #2 – Homegrown AI languages
With the growing demand for Artificial Intelligence, corporations entered the competition for market share and mindshare. Microsoft, Amazon, IBM, and Alphabet started a race for the leading position in the AI-market, and they fight with their very own AI-languages.
Corporations have already understood that enhancing their positions on the AI market takes bold actions. Developing your own AI language is just bold enough. They started releasing software packages for developers, and some, like Uber, went further by developing their own AI-language, Pyro, written in Python.
What’s next? Perhaps fragmentation of a separate AI ecosystem (not unlike iOS vs Android or Mac vs PC). There are going to be new players on the AI arena.
Reason #3 – AI Chipsets
With new AI languages and packages, new AI solutions are definitely going to appear. But what about hardware? The CPUs we currently have are powerful but they don’t meet the standards of machine learning. Modern processors don’t have enough processing units to enable fast connections and computations.
Of course, big companies couldn’t stay aside and entered another race — who is going to develop first powerful AI CPU? Apple, Huawei, IBM, Intel, Qualcomm, and Alphabet are working on a new processor, or, more precisely — a system on a chip (SoC). They are designed specifically to make many connections (just enough for machine learning) and protect processed data.
The most promising claim was made by Elon Musk, when he, late in 2017, said that Tesla is working on a custom AI hardware chip. The competitors, of course, couldn’t just stand and watch. Alphabet came up with its own chip and called it a Tensor Processing Unit (TPU), specifically built for AI development. Moreover, they even tested the technology in a famous AlphaGo match between an AI DeepMind system and world-known Go champion.
How does this challenge developers? On one hand, it gives plenty of possibilities for AI development, on the other hand — we are all going to be forced to choose between different frameworks, hardware, and languages.
Reason #4 – AI Marketplaces
Before, if you developed something cool, you could boast to your friends and colleagues, and maybe publish the code on GitHub (which just got purchased by Microsoft). In 2018, big companies will change this situation by creating organized marketplaces where AI-developers can sell their innovative code to big corporations, and make not only a name for themselves but also some big money.
From a business perspective, it’s a great opportunity. Hiring a good AI-developer in an in-house team is difficult and expensive. Now you could go to the marketplace and buy an AI algorithm the way we buy bread or milk (but much more expensive).
Algoritmia – a marketplace where developers can exchange and sell their AI-algorithms (and another example of AI Cloud, by the way).
DataXu – a marketplace for proprietary algorithms.
Quantiacs – a place where developers can build and sell the algorithmic trading system
Precision Hawk created a marketplace for predictive agriculture algos.
What if you want to build AI into an existing application? Go ahead, with tools like Nara Logics, Clarifai, MetaMind, you can build the algorithms into already developed products.
Reason #5 – Cognitive Computing
We often hear that AI is dumb. It’s enough to monitor the latest project to realize: it won’t be dumb much longer. With natural language processing and AI, algorithms will be able to understand our intention, literally read minds. It’s not science fiction anymore, as proven by IBM’s Watson system that supercharges our abilities to solve complex problems.
With cognitive computing, AI will be able to assist doctors, teachers, scientists, researchers – heck, everyone. Just imagine how smart chatbots could become.
What does this all mean for businesses?
If before business owners could delay AI implementation saying that there are not enough tools, now there are all kinds of frameworks, specialized programming languages, and even hardware to power AI-product. Delaying is really not an option anymore.
Actually, you can start implementing AI today, if you drop us a line. We’ll contact you right away and figure out the most efficient AI-solutions specifically for your business objectives.
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