AI and machine learning (ML) have the potential to transform the way we do business. In fact, PwC believes that artificial intelligence would have a worldwide economic effect of more than $15 trillion by 2030. There are very few technologies that have the potential to have such an impact on the world in the near future.
Here are 5 ways in which AI and Machine learning will change the future;
1. Tinyml makes an attempt to integrate ML and IoT
In recent years, the computing power of AI and Machine learning algorithms has grown too large to be hosted on a local device. Yet, the computational capacity of edge devices that gather data is limited. As a result, developing ML algorithms that can work with less local memory or computational capacity has become critical.
TinyML, in essence, defines any machine learning algorithm that can run on an embedded or edge device in an IoT system. Gadgets have typically been required to acquire a large amount of worthless data. Then transmit it to a cloud provider to be examined by machine learning algorithms. TinyML might fix this by simultaneously tackling two difficulties.
For starters, it enables IoT devices to evaluate data while using little energy and computer resources. Second, it enables these gadgets to capture just meaningful data. This is already seen in daily electronics. For example, most smartphones and home speakers may feature “wake” words.
These phrases (or other keywords, such as the device’s name) compel the gadget to gather data only when necessary (i.e., useful data). The edge computing industry will be large in any case (predictions vary from $40-60 billion by the late 2020s). And TinyML will most likely power the whole market.
A typical microcontroller (a tiny microprocessor that powers local devices such as printers and industrial sensors) costs around 60 cents per unit. If more and more of these low-cost devices can power AI and Machine learning models at the edge, a large amount of data may be gathered and evaluated for very little money.
Thankfully for the IoT economy, ABI Research projects that by 2030, there will be around 2.5 billion devices supplied with TinyML capabilities. Although algorithms are currently commonly developed on bigger servers, many experts believe that over the next five years, models will also be learned on edge devices.
2. Natural language processing powers new AI and Machine learning applications
Natural language processing is one of the most promising commercial applications in the AI and Machine learning sector (NLP). Our world is text-wrapped. Document and text analysis, formatting, translation, and use are critical in all forms of business across the globe. It’s not just words either. NLP is being and will be used to analyze data in ways that are vastly different from previous statistical methodologies.
- So, what exactly is NLP?
It’s simply a mechanism for computers to communicate in our language. Formerly, computers had to convert our human language into code. But, by using NLP, robots can derive insight from our data in its natural condition.
In 2020, humans generated 2.5 quintillion bytes of data each day. And most of it is the text that can be read by humans. Businesses all around the globe use NLP to assess text sentiment, categorize text, extract meaning and keywords from text, and analyze text.
Facebook created an NLP software in 2019 that won first place in a language translation competition. Grammarly, for example, has long used NLP to enhance grammar across a variety of digital situations.
Hundreds of organizations in the legal and commercial sectors have started to use NLP to evaluate and produce thick legal contracts.
For example, e-discovery firm Epiq is currently utilizing NLP to evaluate hundreds of contracts in minutes for customers.
And Epiq is far from alone. ThoughtRiver, Kira Systems, and Luminance are all startups that use NLP to swiftly evaluate legal materials. NLP is currently even being utilized to build whole new papers and articles.
With its launch in 2020, OpenAI’s GPT-3 astounded the globe. NLP algorithms can generate writings that read nearly precisely like those written by humans. Now, OpenAI promises to produce around 4.5 billion words every day.
More than 300 applications utilize the NLP software, and thousands of developers use it to produce content. GPT-3 is projected to produce at least twice as much material as all WordPress blogs on the internet in a single year.
This is particularly astounding given that WordPress runs about 40% of the internet. NLP advancements are still in their early stages, and breakthroughs like GPT-3 will only improve with time.
3. AI and Machine learning is causing a healthcare revolution
AI and machine learning have proven particularly revolutionary in the healthcare business during the past year. AI developments were critical in combating the worldwide epidemic. Moreover, as telemedicine and other health-tech breakthroughs proliferate, AI will become even more crucial.
2020 also revealed flaws in the healthcare sector as it now exists. COVID-19, for example, exacerbated the scarcity of nurses and healthcare workers. Some AI projects, on the other hand, are attempting to address this issue.
According to Accenture, artificial intelligence (AI and Machine learning) will enable nurses to manage 20% more pent-up patient demand in the coming years. This is most likely why 90% of hospitals have some kind of AI project in the works for the foreseeable future. This is a significant rise from less than half of all hospitals in 2019.
2020 was also a watershed moment for healthcare investment. And huge increase was seen in the first quarter of 2021 as well. In the first quarter of 2021, $2.5 billion (more than twice the first quarter of 2020) was invested in AI-focused healthcare firms. According to CB Insights, AI and machine learning in healthcare were discussed on over 2,000 first-quarter earnings calls.
Insitro is one of the most innovative firms combining machine learning with healthcare. AI and Machine learning is used by the firm to examine enormous biological data sets. In 2021, Insitro raised $400 million in a Series C round.
It has also signed partnerships with prominent pharmaceutical companies like Bristol-Myers Squibb and Gilead to do ML-driven drug development. Strive Health is another AI-driven healthcare firm that is making headlines. The firm focuses on renal prevention.
Almost 37 million individuals are affected by kidney disease. Yet just 10% of chronically ill persons are aware that they have the condition. Strive Health is attempting to avoid chronic kidney disease by using artificial intelligence technologies that can predict renal disease development with more than 95% accuracy.
Strive just received $140 million in funding from Alphabet’s venture arm, as well as other investors. One of the most astounding developments has come from a huge tech business that is not even related to the healthcare sector. In 2018, Google’s DeepMind company launched its AlphaFold initiative.
AlphaFold predicts protein structures using DeepMind’s deep learning technology. Simply put, amino acid chains in our bodies fold in various ways to generate various protein structures. Knowing and anticipating these structures may help us comprehend hereditary disorders and other concerns.
Protein may fold into over 200 million distinct shapes, making observational classification of diverse structures almost difficult.
Here’s where AlphaFold and deep learning come into play. When it comes to forecasting future protein shapes, Google’s newest AlphaFold 2 outperformed every other competition.
AI and Machine learning programs such as AlphaFold and Insitro promise to revolutionize how we treat and manage diseases over time.
4. Deep learning aids in detailed data analysis
Deep Learning has probably been the most significant development in the area of artificial intelligence. AI and Machine learning includes Deep Learning. It is essentially a method for robots to replicate the human brain. They do this by constructing layers of artificial neural networks (similar to those found in human brains) and using them to handle vast volumes of unstructured data.
This enables a machine to learn how to categorize or evaluate data without having to be programmed to do so. Although the idea has been around for a while, the “revolution” in deep learning began in 2012.
Deep Learning was employed by a team participating in a Kaggle contest to identify the detrimental effects of chemicals in household products. Following this discovery, the use of machine learning has skyrocketed.
According to IFI Claims Patent Services, the number of machine learning patents is increasing at a 46.01% CAGR. Deep learning applications are many, spanning all sectors and ranging from predictive maintenance to product planning. McKinsey discovered in 2018 that deep learning may be utilized to enhance results in 69% of use cases.
But, adoption is more difficult than it seems. McKinsey estimated in 2020 that just 16% of firms have pushed deep learning initiatives beyond the earliest piloting phases.
This might be changing.
Deep learning has the potential to improve almost every business on the planet. In reality, it is becoming more important in many contemporary items. Consider, for example, self-driving automobiles. Unprecedented situations are a key issue for the autonomous car business.
Several firms train their autos using 3D maps or previous driving data. But, when an unusual event arises on the road, the car is unsure how to respond.
Deep learning, on the other hand, may enable self-driving vehicles to form connections and learn in ways that do not rely on previous experiences. Deep learning is already being used in the products of some of the most sophisticated autonomous driving firms.
Cruise’s co-founder has said that the firm intends to be AI-native. Self-learning software is critical to the future of self-driving cars. In 2016, GM paid $1 billion for Cruise. In 2020, the business attracted investments totaling more than $30 billion from Microsoft, Honda, and others.
You can also predict deep learning’s future potential by looking at who is working on it. And you don’t have to look far to discover that many of the world’s top IT businesses are substantially investing in deep learning. Consider, for example, chatbots. Chatbots have been misbehaving for years, producing repeating replies and generally not sounding human.
Yet, Google now claims to have produced the finest chatbot in the world.
To grasp the context of a conversation, Google Meena uses a neural network (trained on a massive library of social media and internet chats).
And it seems that Google Meena communicates in a manner that is quite similar to human norms.
As we’ll see later, deep learning systems of AI and Machine learning are also driving innovation in fields like natural language processing (NLP) and healthcare. OpenAI’s GPT-3 natural language processing system, for example, has more “neurons” than the human brain.
5. Advanced intelligence benefits human decision-making
People have been concerned about being replaced by AI and Machine learning since its inception. Although some still disagree, many analysts think that in the future, AI will mostly complement human intellect.
This is referred to as enhanced intelligence. The desire for human augmentation of AI is also increasing. Amazon Web Services will launch A2I in 2020, a platform where developers can find people to critique their AI models and systems.
As Amazon says, sometimes a person is required to assess specific results and tasks in order to detect errors or issues that a computer cannot.
The Bottom Line
These are the top AI and Machine learning developments to watch for in the next years. AI and Machine learning will surely alter our way of life. Several of these developments are associated with new methods of data collecting and analysis. As more innovative data is generated (mostly via IoT), new AI and ML projects will transform how we use that data.
