Artificial intelligence (AI) and machine learning (ML) — these are probably the only things we’ve talked about for the last 15 months. We can’t help it. We love this bleeding-edge technology so much that we can’t just shut up about it. The possibilities are limitless. No, really. Think about it. AI is slowly but steadily becoming central to pretty much every industry including healthcare, retail, power and utility, customer service, marketing, manufacturing, education, finance, to name just a few.
If you talk numbers, revenue from AI-based hardware, software, and services is expected to hit approximately 157 billion USD globally by the end of 2020. This is roughly a 12.5% increase in revenue from last year.
As we inch towards the end of 2020 which was nothing short of a nightmare, let’s take a look at some of the AI and ML trends we foresee for 2021.
Gartner calls this an IT mega-trend. Hyper-automation is essentially the idea that anything that can be automated within an organization should be automated. It is also known as digital process automation or intelligent process automation. COVID-19 has hit the accelerator on this trend and there is no turning back from here.
AI and ML are central to and key drivers of hyper-automation. In order to be successful with your hyper-automation initiatives, you cannot rely solely on off-the-shelf software. Why? Processes are unique to each business. These processes may change based on unprecedented circumstances.
A static, off-the-shelf software will be more of a blocker than an enabler when it comes to hyper-automation. It cannot quickly adapt or keep up with the changing business processes. The opposite is possible with a more dynamic AI solution.
Bringing discipline to AI development
What do we mean by that? If you look at data on AI-related projects, you’ll see one recurring pattern: only 53% of all AI projects successfully make it past the prototyping stage to implementation.
Why is this the case? When companies try to deploy a newly developed AI system they usually tend to struggle with issues such as maintenance, scalability, and governance. Most importantly, the initiatives fail to meet revenue goals.
That said, businesses are coming around to the idea of why it’s necessary to understand that a robust AI strategy is perhaps the only possible road to reaping the returns on investments in AI. That means you need to have a disciplined AI engineering process that incorporates DataOps, ModelOps, and DevOps.
AI and ML are making their way into enhancing cybersecurity for corporate and home security. And for the right reasons. We all have a fair idea of how crucial cybersecurity is. Every one of us is racing towards keeping our tech and gadgets safe and free from threats such as malware, ransomware, DDS attacks, etc. AI will be used more extensively to identify these threats and also address them faster.
AI-enabled cybersecurity software can collect data from transactional systems, communications networks, digital activity, and websites, as well as from external public sources, and utilize AI algorithms to recognize patterns and identify threatening activity – such as detecting suspicious IP addresses and potential data breaches.
On the other hand, the use of AI in home security systems is largely limited to systems integrated with home video cameras, voice assistants, etc. However, AI is expected to be used to create smarter homes. These AI systems will learn the preferences of the occupants of the house in order to improve its ability to identify intruders.
IoT or the Internet of Things is a fast-growing area of interest in the last few years. It is expected to grow to 24.1 billion devices in the next ten years and generate up to 1.5 trillion USD in revenue.
Weren’t we talking about AI? Well, the use of AI and ML is closely intertwined with IoT. For instance, AI is applied to IoT to make devices smarter and secure — the two trends we just discussed. However, the benefits are two-way. For AI models to function effectively, it needs large volumes of data. That’s precisely what IoT sensors and devices provide. Win-win.
For example, let’s consider a manufacturing plant. All the IoT networks throughout the plant can collect operational and performance data. This data is then analyzed by AI systems to improve production system performance, boost efficiency, and predict when machines will require maintenance. Would it be safe to say that AI and IoT combined can revolutionize industrial automation?
AutoML is a platform that automates the process of developing machine learning algorithms for various tasks. Yes, we’re talking evolutionary algorithms. That means you write algorithms that can write algorithms. Whether you think it creepy or uber-cool, this is coming our way.
Think about it this way. There’s a learning paradigm in which the machine will randomly generate algorithms and then work to see which ones will perform the best. After several generations, the algorithms become better and better until the machine finds one that performs well enough to evolve. It picks out the best of the lot!
In order to generate novel algorithms that can solve new problems, the ones that survive the ‘evolutionary’ process are tested against various standard AI problems.
Are there any such trends you foresee for 2021? Let us know.
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