4 Revolutionary Innovations in AI

  • Ashesh Anand
  • Aug 25, 2021
  • Artificial Intelligence
4 Revolutionary Innovations in AI title banner

Artificial intelligence innovation has taken fire, becoming the hottest topic on the agendas of the world's leading technology corporations after decades of sluggish burn.

 

What's the source of this fire's fuel? “Faced with a deluge of data, we required a new sort of system that trains and adapts,” says the author. When it comes to technology and innovation that affects people's lives throughout the world, AI is the hottest trend. On one hand, AI innovative trends are emerging, while on the other, it has produced a multitude of work prospects for humans.

 

Technology such as Artificial Intelligence (AI) is transforming the globe, and innovation is never-ending. Over the years, AI has demonstrated promising tendencies and is affecting nearly every business. The COVID-19 Epidemic has prompted shifts in consumer and economic patterns, yet the globe had been steadily adopting AI even before the pandemic. AI has emerged as a pioneer in a variety of areas, including automation strategy and privacy concerns.

 

( Also Read - What is AI? )

 

 

What is AI?

 

Artificial intelligence (AI), often known as machine intelligence, is the subject of research and development that focuses on creating computers and robots that can parse data contextually to offer desired information, provide analysis, or trigger activities depending on results. Companies all around the world are investing in training robots to think more like humans using techniques like machine learning and neural networks.

 

According to Towards Data Science, The two major reasons for the rapid growth of AI in this decade are:

 

1) Data - Thanks to the Internet and IoT devices the amount of data generated is growing exponentially.

 

2) Compute - The hindrance that we faced in the previous decades was solved, which in turn boosted the power of AI. Many companies have started creating hardware specifically for training Deep Learning models.


 

As said by  Arvind Krishna, Senior Vice President of Hybrid Cloud and Director of IBM Research: “What was deemed impossible a few years ago is not only becoming possible, it’s very quickly becoming necessary and expected.”

 


Innovations in AI

 

This blog first examines the social and economic developments brought about by our usage of AI, with an emphasis on the decade following the introduction of smartphones in 2007, which have contributed significantly to "big data" and therefore the efficacy of machine learning and hence also to AI.

 

( Related blog - Big Data analytics )

 

The decade of the 2010s will be remembered for the introduction of Artificial Intelligence, one of the world's most powerful technologies. As more capital is made available for its development and it becomes increasingly embraced by corporations and consumers alike over the next decade, it will be worthwhile to evaluate some of the important milestones that have made this advancement possible over the previous decade.


 

  1. Introduction of Internet of Things (IoT)

 

If you look around the space you're in right now, there's a good chance you'll find at least one piece of IoT equipment. From smart home technologies to voice assistants, technology has transformed how we go about our daily lives. These connected gadgets are ubiquitous, whether it's an Amazon Echo, a smart plug, or a Nest Thermostat. 

 

Though most people remember 2011 for the death of Steve Jobs, it was also a significant year for Apple for another reason. Siri, the company's digital assistant, was also introduced at the same time, a day before the death of Jobs with the launch of the iPhone 4s. Siri signaled the start of the era of virtual assistants. 

 

Apple bought Siri two months after its debut as a mobile application by Nuance Communications and was subsequently integrated into the iPhone 4S. Siri was the first intelligent personal assistant to replace touch displays and keyboards.

 

( Suggested Reading - Big Data in Apple )


 

The IoT sector has flourished as a result of this innovation. The emergence of AI assistants has sparked a surge of voice-controlled smart home gadgets that can be managed with a single command. 

 

No one had access to these tools just five years ago. It's difficult to go a day these days without asking Google for directions or Alexa to set a timer. All of this is made possible by artificial intelligence (AI), which can detect what a user is saying, interpret it, and reply properly. 

 

A.I. may go even farther when used in combination with other IoT devices. A.I. has given us the ability to regulate the temperature in our houses and even see who is at the door without even being at home, thanks to the creation of different interconnected home gadgets.

 

It's astounding to think about how far the Internet of Things will extend in the 2020s, given the tremendous improvements achieved in this decade alone.


 

  1. Beginning of Deep Learning

 

Although Deep Learning has a history dating back to the 1940s, it was only in the 2010s that it became established and widespread. Deep learning technologies have invaded nearly every area of technology, and you've most certainly utilized them without even realizing it. 

 

( Recommended blog - Deep Learning Applications )

 

Today's AI systems would not be nearly as strong if it weren't for it. By combining various "layers" of knowledge, deep learning algorithms enable AI to extract complex information from basic inputs. For example, it enables image-recognition algorithms to recognize a complete image based on basic lines, colors, and forms.

 

 


 

Some of the fields of Deep Learning are - 

 

  • Language Recognition 

Deep learning algorithms can now recognize and comprehend not just the vast majority of languages, but also a broad range of dialects within a single language. A.I. devices can recognize the language in which a person is conversing. The more a deep learning system hears it, the more likely it is to activate another A.I. that specializes in the dialect being spoken and carry out any voice instructions.

 

Deep learning AI may also be used to automatically translate written and spoken content. Google Translate can recognize the languages of written text from even an image and translate it in real-time, which is quite handy for tourists!


 

  • Text Generation 

 

Text generation artificial intelligence has now made it possible for anybody, or rather any computer, to become a writer. Machines can examine the content, syntax, and grammar of any piece of writing using deep learning algorithms. It may then continue producing fresh content without any human input and produce its own piece of text after it has learned this.

 

( Related blog - Text Generation with Markov Chain )

 

This method is quite similar to previous software that examines data sources and information inside a piece of writing and uses that information to automatically build data visualizations.



 

Deep learning became indispensable in the 2010s, coinciding with the meteoric development of Big Data. AI systems were able to learn more (and quicker) than ever before without the need for human programming because of enormous libraries of raw data.


 

  1. Age of Automation 

 

Google started its first self-driving vehicle experiment in 2010, right at the start of the decade. Waymo was born out of it, and it is now paving the road for a slew of other firms to follow suit. Though the 2010s were the decade of artificial intelligence, it's not unrealistic to expect the 2020s to be the decade of self-driving automobiles.

 

The technology has already advanced to the point where certain automobiles can fully drive autonomously in test settings. Carmakers will almost certainly have fully autonomous vehicles on the market within the next several years. Though it may take some time to catch on, the future of driving appears to be almost hands-free.

 

( Also Read - How Tesla Uses AI )

 

Self-driving automobiles, on the other hand, would not be conceivable without AI.

 

It not only directs them where to travel, but it also identifies obstructions, avoids collisions, and keeps a continual eye on the vehicle's surroundings. Something that previously appeared like a dream when we saw them in science fiction movies has become a new human reality thanks to AI.


This image depicts that how automated vehicles move on the road by the help of specialized sensors and AI mechanism.

Automated vehicles moving on the road



According to IntellYGenz, Businesses and customers alike are benefiting from the benefits of A.I. automation, which is not limited to automobiles. IPA stands for "Intelligent Process Automation," which automates workflows and procedures to make activities easier to complete without the need for human intervention. Processing transactions, initiating automated reactions, interacting with systems, and a variety of other activities are examples of these tasks.


 

  1. A.I. Robotics

 

We are certainly living in The Future, with self-driving vehicles, smart houses, and now A.I. robotics on the horizon. These artificial intelligence advancements are now being employed in robotics to bring automation to a physical level, rather than merely a digital one. Most of us have heard about Sophia, the A.I. robot who became the world's first robot citizen.

 

Robots have become increasingly common in many sectors of industry over the last decade, making production and development easier, safer, and more efficient than ever before. Robots are quickly becoming an integral part of our daily life, from assembly and packing to customer support bots.


This image depicts different sectors implying AI and creating innovative technologies such as Healthcare, Autonomous Vehicles, Security and Defense, Manufacturing, Education, Entertainment, Banking and Finance, and Workplace.

Different Sectors Implying Innovations in the field of AI


In the decade between 2010 and 2020, we also saw some other great Innovations.

 

  • DeepMind 

 

DeepMind, out of all the firms undertaking incredible AI work, deserves its own spot on this list. Demis Hassabis co-founded Deepmind Technologies, a UK artificial intelligence startup. This firm has made a significant influence on AI over the last ten years. In 2014, when Google invested $500 million on a little-known AI firm named DeepMind, many people felt the corporation had gone insane. Now, half a decade later, that decision is paying off.

 

DeepMind is best known for its game-playing AI systems, which are capable of defeating professional gamers regularly.

 

( Also Read - 5 Game Developer Companies integrating AI research )


 

In early 2019, a DeepMind-powered AI vanquished players in “Starcraft II,” a noteworthy achievement. The strategy game is well-known for its complexity and difficulty in mastering it. DeepMind, on the other hand, easily defeated professional players. That AI has a mind-boggling amount of "thought" built into it.

 

Though AI systems that play games aren't exactly altering the world, they do have value.

 

 

  • IBM Watson Won Jeopardy 

 

Since 1964, “Jeopardy!” has been a hugely successful trivia game program. No one could have predicted AI's rise to prominence, let alone its appearance on the show, back then. Most people were surprised when IBM's Watson defeated former "Jeopardy!" winners, Ken Jennings and Brad Rutter. Although attention-getting AI spectacles are prevalent these days, this win was unique.

 

“I had been in A.I. for a while,” Jennings explained and understood that the type of technology that might beat a person in a game of ‘Jeopardy! ' was still decades away... Or so I thought.”
 

Watch this video for more on such innovations in AI:



  • Image NeT Challenge

 

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is a large-scale evaluation of methods for object identification and picture categorization. To date, this has been the standard against which the classification model's performance has been measured. It also offered the information needed to train large models.

 

At the sixth iteration of the ImageNet Large Scale Visual Recognition Challenge in 2015, Microsoft and Google computers outperformed humans in picture recognition. Deep learning techniques enabled the robots to recognize photos and objects in over 1,000 categories, outperforming humans. The algorithms were created using several types of artificial neural networks that emulated the human brain's operation.

 

This exciting new discovery enables intelligent systems to automate activities that involve item or person detection and then a choice about how to continue based on that recognition.

 

 

  • TensorFlow Releases 

 

Tensorflow, Google's deep learning platform, was open-sourced, and this was a game-changer since it offered everyone the ability to construct amazing models. TensorFlow is an open-source machine learning platform that runs from start to finish. It includes a large, dynamic wide range of tools, libraries, and community resources that enable academics to push the boundaries of machine learning and developers to quickly develop and deploy ML-powered apps.
 

 

  • Open AI Breakthrough 

 

Elon Musk established OpenAI, a non-profit foundation that does development in the field of artificial intelligence. It primarily focuses on deep reinforcement learning.

 

Related blog - OpenAI GPT-3

 

Because algorithms often work independently, OpenAI's innovation ushered forth a crucial new path for AI. “This is a step toward developing AI systems that fulfill possibly the best goals in chaotic, difficult circumstances involving actual humans,” the OpenAI researchers said. Many AI milestones have now been attained as a result of these discoveries, prompting us to consider the potential of experiencing superintelligence in our lifetime.


 

Conclusion

 

You can see the influence AI had on the decade; the majority of them were beneficial, but there were a few that had a detrimental impact on society. AI is neither good nor bad in and of itself; it is up to us humans to put it to good use. The advancement of AI does not appear to be slowing down anytime soon. The AI Winter does not appear to be occurring any longer. AI is progressing at a breakneck speed, thanks to more data, computing, and study in the area. One thing is certain: AI is here to stay, and we won't be able to avoid it, so we'd best learn to race with it.

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