Decade Of Artificial Intelligence: A Summary

The world has seen a boom in the field of Artificial Intelligence in the past few years & What our AI community has achieved in the last decade has set a strong foundation for the future

GraphQL has a role beyond API Query Language- being the backbone of application Integration
background Coditation

Decade Of Artificial Intelligence: A Summary

In the last few years, the world has seen a boom in the field of Artificial Intelligence. That’s because of the various factors such as:

1. Availability of hardware, courses, platforms, big companies taking workshops, etc. 

2. Data: Huge data collected by different companies, IoT devices, etc

3. Computation: Availability of AWS, Google Cloud, NVIDIA GPUS, etc

Thanks to the vast research and development conducted in the past decade, AI has become a prime technology in today’s world and is expected to change the future with its wide range of applications. 

In this post, I’ll take you through various different stages of AI development in the past decade.

2010

a) DeepMind

DeepMind Technologies is a UK based artificial intelligence company, co-founded by Demis Hassabis and had a big impact in the field of AI.

b) ImageNet Competition

ImageNet competition brought a lot of development in the field of computer vision. Designed for research in object recognition, it contains 14 million images hand-annotated for 20,000 categories, like a balloon, chair, etc. It is held annually. The primary goal of the competition is to reduce the error rate in identifying categories like chairs from the image. It was the brainchild of the renowned professor Fei-Fei-Li.

2011

a) IBM wins Jeopardy

IBM Watson is a question-answering computer system. It beats two former Jeopardy champions. This was a gigantic leap of AI in Natural language processing. It shows AI reached the level of understanding the different intricacies of human language.

2012

a) Convolutions Galore

In one of the competitions of German Sign language recognition, AI build with the help of CNN’s beats the Human accuracy of 99.2% The accuracy which AI achieved was 99.46%. A similar groundbreaking feat was achieved in the ImageNet competition where the University of Toronto reduced the error rate from 25% to 16%. It was this period when AI started getting better than humans in computer vision tasks.

2013

a) Google Glass


Google Glass is a brand of smart glasses. It was developed by X with the mission of producing a ubiquitous computer. It is an optical head-mounted display in the shape of glasses. This created a pavement for a new hardware device that will be collecting data.

Google Glass joined the companies of Laptops and mobiles. Today, along with them we have other data collecting partners too like AI voice assistants, Smartwatches, and lots of other sensory data collectors.

b) Boston Dynamics revealed Atlas

Atlas was revealed to the public by Boston dynamics. The future is robots being a family member. There are few like Alexa, Mop P who have already entered our houses and there are many who will join the family in the future. Boston dynamics today is a pioneer in the field of Robotics and it all started with the popularity of Atlas.

2014

a) GANs

GANs is the baby of AI pioneer Ian Goodfellow. It is the next big thing in the field of computer vision. In simple terms, GANs generate new data from the existing one and have remarkable applications in the major industries. All the aging apps, refacing apps, cartoonifying your images are the different applications of GANs.

b) Google acquires DeepMind

The acquisition of DeepMind by Google is a landmark moment in the field of AI. The rise of Google in AI after this acquisition is noteworthy. Facebook too was in the race but Google won it at a whopping amount of 400 million euros.

2015

a) Tensor flow release

Tensor flow is an end to end open-source platform for machine learning. It is very popular because of its sizable community, lots of packages, tools, etc. The release of Tensor flow made deep learning go mainstream. Lots of people started trying their hands-on deep learning and many different applications and use -cases were seen growing.

b) Open AI founded

Elon Musk founded Open AI. Their work in the field of deep reinforcement learning is remarkable. Today Elon is one of the biggest drivers in the field in AI and Open AI is a big name in the AI community.

2016

a) AlphaGo beats World Champion Lee Sedol

Go, a Chinese game is more complex than Chess. It involves lots of strategies, techniques. An AI learning all these techniques and beating a current World Champion 4/5 is a monumental achievement. There was one very interesting moment in the match when AlphaGo made a move and seeing it the world champion Lee Sedol was so perplexed that he left the arena.

b) Sophia Robot

Sophia is a robot developed by Hong-Kong based company Hanson Robotics. She became the first robot to get the Citizenship of Saudi Arabia. She can imitate facial expressions and engage in conversation with other humans.

c) Release of Pytorch

Pytorch is an open-source framework for building AI models developed by FAIR (Facebook’s AI research lab). Currently, it is the most popular framework for deep learning.

2017

a) AI developing its own language

FAIR i.e. Facebook’s AI research lab trained two AI chatbots to converse with each other. While conversing those chatbots deviated from the primary English language and created their own language. Although the project was shut down, this incidence shows the AI reaching the human general intelligence.

b) ONNX

To enable AI frameworks interoperability, many big companies like Facebook, Google joined hands and launched ONNX — the interoperability ML framework.

2018

a) Self-driving car – Waymo

Waymo is a company that works on self-driving cars. With 10 million miles on public roads and 7 billion in simulation, they are on their way to make the most experienced driver.

b) Google’s BERT

BERT stands for Bidirectional Encoder Representations from Transformers. It can be used in solving many language tasks like questions — answering, sentence completion, etc.

c) DeepFakes

With GANs going mainstream, people have started creating fake videos of famous personalities. It is very difficult to identify just by seeing if the video is real or fake. The technology is called DeepFakes.

2019

a) AI beats Human Doctors in Cancer identification

Google researchers have created an AI system in detecting lung cancer and their accuracy is better than doctors.

b) Open AI beats DOTA2 esports world champions

Dota2 is not an individual game but a team game. The AI needs to coordinate with other AIs to beat any player in DOTA2. The factor of AI coordination and the win over the world champions, highlights the real achievements of it.

The last decade set up a strong foundation for the coming years and as Andrew Ng stated, “AI will be the new electricity and will impact the lives of every individual in the coming years.”

Conclusion

AI has become a prominent part of our life. From suggesting us as per our ordering patterns to replying our queries with the help of ML, AI has become the new normal.

In the last few years, AI has developed at an extremely rapid pace. It won’t be wrong to say that soon we will have our own JARVIS (Yes, from Iron Man) acting as our personal assistant. But there's still a long way to go. 

In this post, I’ve explained what the timeline of AI development looks like and what you can expect in future. Do let me know if I’ve missed any phase in the comments. Till then, happy learning. 

Want to receive update about our upcoming podcast?

Thanks for joining our newsletter.
Oops! Something went wrong.

Latest Articles

Implementing Custom Instrumentation for Application Performance Monitoring (APM) Using OpenTelemetry

Application Performance Monitoring (APM) has become crucial for businesses to ensure optimal software performance and user experience. As applications grow more complex and distributed, the need for comprehensive monitoring solutions has never been greater. OpenTelemetry has emerged as a powerful, vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry data. This article explores how to implement custom instrumentation using OpenTelemetry for effective APM.

Mobile Engineering
time
5
 min read

Implementing Custom Evaluation Metrics in LangChain for Measuring AI Agent Performance

As AI and language models continue to advance at breakneck speed, the need to accurately gauge AI agent performance has never been more critical. LangChain, a go-to framework for building language model applications, comes equipped with its own set of evaluation tools. However, these off-the-shelf solutions often fall short when dealing with the intricacies of specialized AI applications. This article dives into the world of custom evaluation metrics in LangChain, showing you how to craft bespoke measures that truly capture the essence of your AI agent's performance.

AI/ML
time
5
 min read

Enhancing Quality Control with AI: Smarter Defect Detection in Manufacturing

In today's competitive manufacturing landscape, quality control is paramount. Traditional methods often struggle to maintain optimal standards. However, the integration of Artificial Intelligence (AI) is revolutionizing this domain. This article delves into the transformative impact of AI on quality control in manufacturing, highlighting specific use cases and their underlying architectures.

AI/ML
time
5
 min read