The Future is Almost Here- Exploring the Top 9 Trends of AI Innovations

Author : Thangaraj Petchiappan

When Tesla launched Model 3 with Autopilot systems for driving, it was a dream come true for us.

But do you know how it works?

It relies heavily on LIDAR sensors and cameras for navigation. Furthermore, it has AI models integrated into the mainframe for analysing the sensory data for making simple right turns.

Every concept that may have seemed far-fetched can be brought into real life with the rise of automated technology. A cyberpunk city with buildings on clouds? You can see the photos on Dall-E.

Yes, this isn't a dream. It's the world we're quickly moving towards, thanks to new developments in Artificial Intelligence (AI).

Read on as this blog explores the latest AI trends that are changing our world. We will skim through smart language systems and predictive AI tools. Let us see how these are making industries and our lives better.

GPT-3.5/GPT-4 Language Models

Hands down, this changed everything. These incredibly proficient AI systems can understand and write text like humans, yes, almost. When GPT-3.5 came out in 2022:

• it could chat in real time
• people would ask questions, and it would answer them with high precision and quality
• could even write blogs/articles about numerous topics

Then, GPT-4 was released in 2023 and got a boost. It can understand pictures and text, explain images, and even write computer code.

These AIs are already being used in almost every industry. For example, the language learning app Duolingo uses GPT-4 to teach languages better. GitHub Copilot, which uses similar technology, helps programmers write code faster.

Natural Language Processing

NLP has come a long way since its inception in the 1950s. But do you know how this technology evolved to become an integral part of our daily lives?

Language processing began with basic rule-based systems, such as the Georgetown-IBM project. Here, specialists tried to translate Russian phrases into English. ELIZA was one of the first chatbots dating back to the 1960s. It used pattern matching to imitate conversations.

Today, various processing models power a myriad of applications. Google Translate can now cover almost 100 languages with impressive accuracy. As NLP continues to advance, we can expect more natural and meaningful interactions between humans and machines. This is sure to transform industries from IT to education and beyond.

Chatbots and Virtual Assistants

Everyone has access to Siri or Google Assistant, or Alexa nowadays. These AI chatbots and virtual assistants are much smarter than before. They can now understand your context of the topic. Various platforms even remember past conversations and can detect emotions in text or voice.

Companies in almost every sector employ these smart chatbots. They automatically answer customer questions and aid human customer service workers. For us, these assistants can set reminders and even control smart home devices.

Explainable AI

Will AI take over our world?

We might not know for sure. But we do have an idea how it works.

All AI systems get more and more complex every day. They are utilized in almost every area like healthcare and finance. Thus, people need to understand how these systems make decisions. This is where Explainable AI comes in.

The Explainable Artificial Intelligence (XAI) initiative was announced in 2016. It attempted to develop more explainable models while keeping high-performance levels.

Companies such as IBM and Google are pioneering research models. IBM's AI Explainability 360 toolbox includes methods for explaining machine learning model decisions. Google's What-If Tool enables users to see and analyse machine learning models.

Predictive AI Analytics

You can say this is all about guessing the future!

Predictive analytics uses AI to look at past data and guess what might happen in the future. This is changing how businesses make decisions.

In 2008, Google launched its Flu Trends project. They use search queries to predict flu outbreaks. While it faced setbacks, it demonstrated the potential of predictive analytics on a large scale.

Similarly, in retail, companies like Amazon use predictive analytics to guess what products customers might want to buy next. This helps them manage their inventory better and create personalized marketing.

Insurance companies use predictive analytics to better understand risks.

For example, Progressive Insurance looks at driving data to offer personalized insurance rates.

In healthcare, predictive analytics helps improve patient care. The University of Pennsylvania Health System uses AI to predict which patients might get sepsis. This early warning system has helped save multiple lives.

Generative Adversarial Networks

Go to Dall-E and type: A futuristic city skyline at sunset with flying cars and towering skyscrapers in the style of cyberpunk art.

It will generate a series of images that would feel very accurate. You’d think it is a photograph! These models were introduced in 2014 by Ian Goodfellow and his colleagues.

So, basically, GANs AI models that can create hyper-realistic multimedia. This involves two neural networks - a generator and a discriminator. They compete against each other to create highly realistic synthetic data.

Today, GANs are pushing the boundaries of creativity and reality. DeepArt uses GANs to transform photos into artworks in the style of famous painters. NVIDIA's StyleGAN can generate incredibly realistic human faces that don't exist in reality.

AI in the Medical Field

The journey of AI in healthcare began in the 1970s, yes, fifty years ago!

An expert system like MYCIN was developed which could diagnose blood infections. However, these early systems were limited by the computing power and data available at the time.

Fast forward to 2016, research found that a deep learning system could diagnose diabetic retinopathy in retinal pictures with comparable accuracy as human specialists.

Today, AI is improving almost every aspect of healthcare. Various algorithms may now diagnose illnesses quickly and reliably. Google Health has created an artificial intelligence system that can identify breast cancer in mammograms.

AI in Education

The notion originated in the 1920s with Sidney Pressey's teaching machines. However, the true integration of AI into education began in the 1980s with Intelligent Tutoring Systems.

In 2011, Sebastian Thrun and Peter Norvig made their Stanford AI course available online for free and:

• It gathered more than 160,000 students from 190 nations.
• Pioneered Massive Open Online Courses (MOOCs)
• Showcased AI's ability to scale education

Thus, artificial intelligence is revolutionizing education in many forms. Learning experiences may be personalized to meet the requirements of individual students. Adaptive learning platforms utilize artificial intelligence to change the difficulty and material of coursework in response to a student's performance.

For example, Carnegie Learning's MATHia employs artificial intelligence to give tailored arithmetic training. This methodology adapts in real time to each student's learning style and pace. Similarly, Duolingo personalizes language instruction using artificial intelligence (AI). It even adjusts the difficulty level of each chapter. Then, it reviews concepts based on the learner's performance to make the learning feel personal.

BIOMETRICS & AI

Alphonse Bertillon created a system of physical measures for identification in law enforcement. This marked the start of biometric instruments. With the invention of automatic fingerprint recognition technologies, the modern era of biometrics began.

But the credit goes to the Apple!

• In 2013, Apple released Touch ID, which brought fingerprint recognition to mainstream consumer products.
• Face ID was introduced in 2017. It set benchmarks in face recognition technology.

The twenty-first century saw remarkable advances in biometric technologies. Today, AI-powered biometric technologies are revolutionizing security. They are required for authentication across several sectors. Similarly, facial recognition technology is widely employed everywhere.

All of these AI models can be employed to detect fraudulent activity by studying how users interact with their gadgets. It considers elements such as typing rhythm and mouse motions.

Edge AI

Edge AI refers to operating AI locally on devices rather than on the cloud. This speeds up the process while also increasing privacy.

Yes, smartphones are leading the way in Edge AI. Apple's M3 silicone chips have dedicated parts that are just for AI tasks. This often enables features like Face ID and Siri. Samsung recently launched the Circle to Search option in their S24 models. This feature utilizes both the hardware and software components of the chip to provide a quick option to find things in photos.

Wrapping Up

As we have seen, AI is influencing practically every aspect of our lives and careers. From sophisticated language systems to assisting physicians and teachers, the future that AI promised is swiftly becoming a reality. The AI revolution is about how we as a society choose to use these powerful tools, not simply the technology itself. The future is indeed almost here. Yes, it promises to be more exciting and transformative than we ever imagined.