HISTORY OF ARTIFICIAL INTELLIGENCE
- 1943 : Evolution of Artificial Intelligence.
- 1950 : Turing Machine
- 1956 : Birth of AI Dartmouth Conference
- 1966 : First chatbot – Eliza
- 1972 : First Intelligence Robot – WABOT 1
- 1974 -1980 : First AI winner
- 1980 : Expert System
- 1987 -1993 : Second AI winner
- 1997 : IBM deep blue – First computer to beat a world chess champion
- 2002 : AI in Home – Roomba
- 2011 : IBM s Waston wins a quiz show
- 2012 : Google Now
- 2014: Chatbot Eugene goostman wins turing test
- 2015 : Amazon Echo
Artificial Intelligence
It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
John McCarthy is one of the “founding fathers” of artificial intelligence.
Approaches of Artificial Intelligence
- Human approach:
– Systems that think like humans
– Systems that act like humans
- Ideal approach:
– Systems that think rationally
– Systems that act rationally
TYPES OF ARTIFICIAL INTELLIGENCE
- Weak AI
- Strong AI
Weak AI
- also called Narrow AI or Artificial Narrow Intelligence (ANI)—is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. ‘Narrow’ might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and autonomous vehicles
Strong AI
- Strong AI- is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Weak AI Strong AI Super Intelligence (ASI)—also known as superintelligence—would surpass the intelligence and ability of the human brain. The best examples of ASI might be from science fiction, such as HAL, the superhuman, and rogue computer assistant in 2001: A Space Odyssey.
What is DEEP LEARNING and MACHINE LEARNING
- Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adapt with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks to mimic the learning process of the human brain
DEEP LEARNING VS. MACHINE LEARNING
Deep learning
• A subset of machine learning
• Requires large amounts of data
• Learns on its own from the
environment and past mistakes
• Longer training and higher
accuracy .
• Makes non-linear, complex
correlations
• Needs a specialized GPU (graphics
processing unit) to train
Machine learning
• A subset of AI
• Can train on smaller data sets
• Requires more human
intervention to correct and learn
• Shorter training and lower
accuracy
• Makes simple, linear correlations
• Can train on a CPU (central
processing unit)
ARTIFICIAL INTELLIGENCE APPLICATIONS
- Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format.
- Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms.
- Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action.
- Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
- Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
DISADVANTAGES OF ARTIFICIAL INTELLIGENCE
- High Costs
- No Creativity
- Unemployment
- Make Humans Lazy
- No Ethics
- Emotionless
- No Improvement
ADVANTAGES OF ARTIFICIAL INTELLIGENCE
- Reduction in Human Error
- Zero Risks
- 24×7 Availability
- Digital Assistance
- New Inventions
- Unbiased Decisions
- Perform Repetitive Jobs
- Daily Applications
MARKET CAPITALIZATION OF AI :
| Report Attribute | Details |
| Market size value in 2023 | Around USD 200 Billion |
| Revenue forecast in 2030 | Around USD 2 Trillion |
| Growth rate | CAGR of 37.3% from 2023 to 2030 |
| Country scope | U.S.; Canada; Russia; Mexico; Germany; U.K.; China; Japan; India; Brazil |
| Key companies profiled | Google LLC; H2O.ai.; IBM Watson Health; Intel Corporation; Iris.ai AS.; Lifegraph; Microsoft; NVIDIA Corporation; |
Top 5 AI companies by their own market capitalization:
| COMPANY NAME | MARKET CAP | SHARE PRICE (current moment) |
| Microsoft | $2.429 T | $326.79 |
| Alphabet (Google) | $1.622 T | $122.87 |
| NVIDIA | $957.61 B | $387.70 |
| Tesla | $774.62 B | $244.40 |
| IBM | $122.85 B | $135.30 |
TOP 15 AI COMPANY IN INDIA
1.Tata Elxsi
2.Zensar Technologies
3.Happiest Minds Technologies
4.Persistent Systems
5.Saksoft
6.Cyient Ltd
7.Infosys Limited
8.Affle (India) Limited
9.Kellton
10.Oracle Financial Services Software
11.Tata Consultancy Services Limited
12.Wipro
13.HCL Technologies
14.Infosys
15.Tech Mahindra