Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning
The Term Artificial Intelligence was coined in the year 1956 by John McCarthy, AI involves machines that can perform tasks recursively that are characteristic of human intelligence. While this is rather general, it includes things like planning, sounds, learning, and problem-solving and understanding language, recognizing objects
We have categorized AI in 2 categories: –
General: – General AI would have all of the characteristics of human intelligence, including the capacities mentioned above
Narrow: – Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI.
It is a sub-field of AI, whose main aim is to use data to train computer algorithms to perform tasks that typically cannot be done or very difficult to accomplish through hard wiring the logic into a program because no one is quite sure about the rules. Recognizing objects in images is one such task where the rules are unclear.
In the 1980’s to early 1990’s, people working in AI often hand code the rules of “intelligence” in “rule-based” systems in an attempt to get the system to appear intelligent. However, as described above, rules are often fragile due to its exceptions and different conditions. It may also be unclear what the rules are.
If we can hand code the rules, it is a huge pain to have to craft them for different domains and applications, hence the huge shift towards using ML algorithms in solving AI tasks. AI, unlike ML, need not require the use of data. So long as you can convince the user that your system exhibits intelligence, anything goes.
AI & Machine Learning in Risk Management
We have developed a platform that uses AI and spatial analytics to predict road transport risks such as historical incidents and near-misses, hyper-local weather data, real-time traffic information and driver risk profiles and then automatically generates risk mitigation intervention suggestions. Organizations such as gas delivery companies can use this information to reduce the risk of accidents.
AI & Machine Learning in unsupervised learning
We are developing intelligent machines capable of unsupervised learning. The company combines leading research on algorithms that mimic the workings of the human brain to learn independently with cutting-edge chip design to support an array of applications.
AI & Machine Learning in Branding
It is an AI-powered platform that helps brands, advertisers and media outlets find and license user-generated social media content. It is using AI-tagging and machine learning algorithms to identify the most relevant content. Clients can plug their social media accounts into the platform to search for content from registered contributors, or the software can reach out to the creators of unlicensed content.
AI & Machine Learning in Security
It uses machine learning algorithms to spot patterns and catch cyber criminals before they can hit. Cyber threats are created at a rate too fast for security companies to keep up to date. It uses AI techniques to learn what is normal within a company’s network so that it can quickly identify anomalies. This allows it to detect cyber-attacks of a nature that may not have been observed before, the unknown unknowns.
AI & Machine Learning in Drug Development
It is focused on applying AI to speed up the process of drug development. The idea is to expedite the traditional research process by ingesting huge amounts of data and literature using proprietary algorithms and NVIDIA’s DGX-1 deep learning supercomputer. It builds tools on top to match the workflow of drug discovery scientists so that they can easily mine and interrogate the data, ideally reducing the time to market for important drugs.
AI & Machine Learning as a Tutor
It is an AI language coach that provides a personalized learning experience for students based on their individual needs. It is a reflection of a teaching strategy focused on intelligent quiz-based learning. Students are given regular small quizzes that are then turned into detailed “brain maps” that provide tailored instructions and evolve as they improve.It learns what each particular student knows and then provides tailored guidance on how to fix their mistakes and improve in the areas where their abilities are lacking. Teachers receive detailed reports on the progress of their students in specific areas of strengths and weaknesses. They can then customise lesson plans to suit the needs of their students.
Now in the market, everybody is talking about Artificial Intelligence, Machine Learning …such high-frequency words. So, what are those? and how it affects our day to day life? Why it is required and why everybody is talking about?
Don’t worry, we will make you understand what are those and how it will be helpful for you. So let’s discuss one by one.
Don’t worry, we will make you understand what are those and how it will be helpful for you. So let’s discuss one by one.
The Term Artificial Intelligence was coined in the year 1956 by John McCarthy, AI involves machines that can perform tasks recursively that are characteristic of human intelligence. While this is rather general, it includes things like planning, sounds, learning, and problem-solving and understanding language, recognizing objects
We have categorized AI in 2 categories: –
General: – General AI would have all of the characteristics of human intelligence, including the capacities mentioned above
Narrow: – Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI.
It is a sub-field of AI, whose main aim is to use data to train computer algorithms to perform tasks that typically cannot be done or very difficult to accomplish through hard wiring the logic into a program because no one is quite sure about the rules. Recognizing objects in images is one such task where the rules are unclear.
In the 1980’s to early 1990’s, people working in AI often hand code the rules of “intelligence” in “rule-based” systems in an attempt to get the system to appear intelligent. However, as described above, rules are often fragile due to its exceptions and different conditions. It may also be unclear what the rules are.
If we can hand code the rules, it is a huge pain to have to craft them for different domains and applications, hence the huge shift towards using ML algorithms in solving AI tasks. AI, unlike ML, need not require the use of data. So long as you can convince the user that your system exhibits intelligence, anything goes.
AI & Machine Learning in Risk Management
We have developed a platform that uses AI and spatial analytics to predict road transport risks such as historical incidents and near-misses, hyper-local weather data, real-time traffic information and driver risk profiles and then automatically generates risk mitigation intervention suggestions. Organizations such as gas delivery companies can use this information to reduce the risk of accidents.
AI & Machine Learning in unsupervised learning
We are developing intelligent machines capable of unsupervised learning. The company combines leading research on algorithms that mimic the workings of the human brain to learn independently with cutting-edge chip design to support an array of applications.
AI & Machine Learning in Branding
It is an AI-powered platform that helps brands, advertisers and media outlets find and license user-generated social media content. It is using AI-tagging and machine learning algorithms to identify the most relevant content. Clients can plug their social media accounts into the platform to search for content from registered contributors, or the software can reach out to the creators of unlicensed content.
AI & Machine Learning in Security
It uses machine learning algorithms to spot patterns and catch cyber criminals before they can hit. Cyber threats are created at a rate too fast for security companies to keep up to date. It uses AI techniques to learn what is normal within a company’s network so that it can quickly identify anomalies. This allows it to detect cyber-attacks of a nature that may not have been observed before, the unknown unknowns.
AI & Machine Learning in Drug Development
It is focused on applying AI to speed up the process of drug development. The idea is to expedite the traditional research process by ingesting huge amounts of data and literature using proprietary algorithms and NVIDIA’s DGX-1 deep learning supercomputer. It builds tools on top to match the workflow of drug discovery scientists so that they can easily mine and interrogate the data, ideally reducing the time to market for important drugs.
AI & Machine Learning as a Tutor
It is an AI language coach that provides a personalized learning experience for students based on their individual needs. It is a reflection of a teaching strategy focused on intelligent quiz-based learning. Students are given regular small quizzes that are then turned into detailed “brain maps” that provide tailored instructions and evolve as they improve.It learns what each particular student knows and then provides tailored guidance on how to fix their mistakes and improve in the areas where their abilities are lacking. Teachers receive detailed reports on the progress of their students in specific areas of strengths and weaknesses. They can then customise lesson plans to suit the needs of their students.
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