Artificial intelligence (AI) is all around us. I didn’t make that statement because I read it somewhere from a popular news website or from Twitter. We don’t have to go far for AI services; while writing this article, I’ve likely spoiled the first trend-that people don’t talk about something that has been an invisible guiding hand.
I’m using AI services right now while typing this text. I unlock my phone through my face because of AI. A sleeping app might use the data on how I sleep to organize my nighttime routine. The smartwatch on my wrist checks my pulse and offers breathing techniques to relax me, especially if my pulse seems like I’m having anxiety.
Through machine learning (ML), an author like me can publish the text that you’re reading right now through the Internet. Advanced routing and software define networks constantly process new ways to deliver this text faster. These technologies are also the same ones used to make sure that the advertisement you see next to this text is something interesting to you.
The package I’ve yet to open today-and the logistics systems in place so I can receive it-benefit from a highly optimized and efficient AI. Opening my online account also means that such AI can recommend how to allocate my budget on expenses categories. And taking a photo of the Moon overhead means that my phone’s artificial intelligence, through machine learning will use high-tech computational photography.
Unlike in the past, AI can effortlessly combine multiple frames to create a beautiful, high-resolution image, despite having no tripod and using small lenses. This is not possible ten years ago. I can also open some old photos, and AI aids me if I want to get rid of noise, do advanced upscaling, or even automatically remove unnecessary objects.
This for me is trend number one: AI is more widespread and more unnoticed. There cannot be an “on-and-off switch” for AI; it assists and keeps on assisting with our daily lives and business activities. We are also growing our dependence on AI’s imperceptible yet beneficial benefits.
Let’s check what other things are going on in the world of AI right now, and what could happen next.
From “AI winters”- which are defined as a period in which there is limited interest and funding to advance AI research- to over-hyping, artificial intelligence for a very long time has been relegated to simply a niche scientific field. The limits of software and hardware technology back then certainly didn’t help, and so it couldn’t be used commercially. Sometimes, a breakthrough might come, but this new hope would fade away, and “AI winters” would crop up again and again.
Nevertheless, by the turn of the 2010s optimism about the benefits of AI has grown tremendously and reached across the world. There was so much talk about how this technology was supposed to be the silver bullet to a host of mankind’s problems: climate change, economic depression, untreatable illnesses, threatening asteroids, and bad drivers. Having pictures of what seems to be advanced and eerily human-looking robotic heads doesn’t help and creates an exaggerated impression of what AI could do.
We reached the summit of all overblown expectations, which we will address in the following sections.
The attitude of practicality
The change that came for both businesses and machine learning experts is a much more grounded expectation from AI about how it can be used to achieve their goals, assist with managing projects, and deciding on techniques. This is already improving AI projects‘ success rates.
What we have right is vastly different from what we’ve been seeing in sci-fi movies or books. The AI that we use is much less fancy, but in various cases, they do work if they’re implemented properly. These initiatives led us to realize how we need to make some major improvements with how we approach data governance and management.
There are much fewer obstacles with using AI because we realized early on how there are problems with data quality-and so they are addressed more efficiently. Being realistic and not pretending that machine learning can solve problems-at least not with sufficient accuracy is a crucial step to provide businesses and teams ways to achieve something together.
It is no longer acceptable for us to see AI as this magic black box that generates decisions, without the skill to answer why such a decision is necessary. This is an essential discussion because we depend on AI more than ever. For example, why did this machine learning model determine that increasing our credit card limit should be denied?
Or why was this route picked over that route when it comes to navigating online? Depending on AI should also mean being safe about the decisions it makes; this attitude is shared among data scientists, who are uncompromising in their view that there shouldn’t be uncontrollable digital AI monsters created.
Explainable artificial intelligence (XAI) seems to be a niche field but is expanding rapidly to become a customary requirement for data projects, especially when machine learning is required to make decisions. This topic is discussed further in a separate article: “Explainable AI (XAI) is what business needs in its path towards Responsible AI.”
Automated machine learning (Auto ML) and the ascent of citizen data scientists
The number of data scientists – people who are proficient in AI remains low, and the pain of this shortage has been borne by businesses. As such, the need for someone more skilled than most in spreadsheets but not possessing advanced knowledge in AI has grown. If given the tools of automated machine learning (Auto ML), cloud technologies, and easy-to-use data software, the ranks of citizen data scientists can grow.
These tools under Auto ML are helpful with simple problems, but complex ones require much more involvement. Regardless, Auto ML continues to improve and deliver better results.
Neural Language Processing (NLP)
Growth of popularity
The reason why this specific field within AI is growing in popularity is it’s attempting to analyze natural rather than mechanical language. Rather than rely on a multitude of human workers to understand their customers for making business decisions, AI is slowly but surely growing its capability to assist with this field.
There’s also what seems to be an arms race in developing better NLP models, the same way GPT has been upgraded continuously to better understand and analyze human-like responses. Conversational context, however, remains a problem within these models, but the situation continues to improve.
GPT-3, however, came as a surprise and was seen as a marked improvement compared to previous models. New models, however, are currently in development and are expected to surpass GPT-3 significantly shortly.
Voice assistants undeniably need more work. My nine-year-old son, when asked about Siri, quipped “Siri’s jokes are not funny at all, but Siri’s bugs are really funny.” Billions of users are expecting their voice assistants to be better at solving their problems, but even the leading voice app Google Assistant needs more improvement. Breakthroughs in this field lay the foundation of a future with a truly touch-free user interface.
Back then, data scientists used to have a separate field, disconnected from the larger software development process. Much of their work is experimental, but with the rise of AI and ML process automation, testing different models is not as hard as before. Coupled with cloud technologies with a scalable model and dedicated infrastructure means that model testing is sped up considerably. However, computational notebooks-an interactive program by which a developer could keep notes and code together in a single document to better see results and note down observations – remain a controversial topic in the face of ML Ops and Data Ops.
The popularity of AI clearly started with implementing it on pattern recognition problems, like texts, videos, voices, and images. AI shines here and is a great tool for both businesses and daily use.
This set of practical applications and technologies will continue to grow and can be used in a variety of areas: helping anti-money laundering and know your customer processes, detecting fraud, analyzing medical images, among others. Improvements in analytical AI will undoubtedly help finance, healthcare, among other sectors, but it is also useful for creative roles.
The growing role of AI in multiple sectors is one of the reasons why we dedicated a whole article on this subject. There is no need to look far to experience the benefits of generative AI; watching TV through modern sets means that AI is helping enhance your viewing experience through framerate enhancers and automatic upscales.
It’s not just TV; the recent trend online is transforming static images of a person into dynamic ones. For businesses, forecasting and future simulation abilities have just been bolstered with AI. It also helps businesses generate likely behaviors through parametrization, which helps optimize processes and strategies, creating a more resilient enterprise. This is also a key trend for 2021, as the world seeks to recover from the pandemic’s effects.
There are dark sides to AI as well. Deepfakes-in which AI technologies replace one face with another can undermine platforms and provide platforms for malicious people to sow misinformation. This is a case of how powerful technology can be used to either benefit or harm people.
Regulations on AI
Despite the fear that more AI could mean that movies like Terminator or Space Odyssey 2001 were right, the reality is that the use of AI is regulated by law; in fact, it is increasingly regulated through time. The European Commission, among other institutions, has recognized the importance of AI and will adjust future legislation accordingly.
One of the questions that AI laws will attempt to resolve is the responsibility of damages if autonomous cars are involved in an accident. The current law seems to have voices of disappointment, with many seeking the government for stricter AI regulation, as they aim to protect their communities from wrongful AI use.
Need more power
AI is beginning to hit the limits of our energy efficiency and computing power. It overcame the limit from traditional CPUs to GPUs, to ML-dedicated GPUs, and later TPUs. Another breakthrough is needed to ensure that AI algorithms can run faster and solve problems better. One solution-quantum computing-is being researched.
The first wave of breakthroughs are coming though: neuromorphic computers, designed to mimic the structure of the human brain are seeing initial success at training natural networks because of their closeness with the brain’s structure of neural networks.
Internet of Things (IoT), Internet of Everything (IoE), and Edge
Our watches, fitness trackers, smartphones, cars, and other electronic devices have features like step tracking, voice, image, and facial recognition, and heartbeat pattern tracking all of these contain some version of AI.
The future is bright for AI
As enterprises learn to realistically implement machine learning projects, more successful digital solutions with grounded outcomes will arise. There will still be no miracles, but incremental progress is still progress, and better outcomes will continue to be delivered. The library of tools, models, data, and technologies grows every day, helping accelerate AI projects to achieve improved results.
AI is not over yet; in fact, it is just beginning. We know better how to use it now, and we have stronger discussions about how to best regulate it. With this foundation, experts are seeing a future major breakthrough in AI. Increasing investment in its popularization and development can happen at any moment. Your business shouldn’t be left behind in AI integration. With an ever-increasing competitive landscape, your business should look ahead to implement better solutions with artificial intelligence.
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