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Machine Learning and AI: The Puzzle is Not Solved Yet

Fifty years ago, a chess-playing programme was considered a form of AI

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Rana el Kaliouby, CEO of the Boston-based artificial intelligence firm Affectiva, is pictured in Boston, April 23, 2018. Affectiva builds face-scanning technology for detecting emotions, but its founders decline business opportunities that involve spying on people.
Rana el Kaliouby, CEO of the Boston-based artificial intelligence firm Affectiva, is pictured in Boston, April 23, 2018. Affectiva builds face-scanning technology for detecting emotions, but its founders decline business opportunities that involve spying on people. VOA

By Nishant Arora

The most buzzed-about disruptive technologies that are changing business landscapes today are Machine Learning (ML) and Artificial Intelligence (AI). Almost all of us have heard or read about them but do we actually know what the fuss is all about?

The enterprises are trying to harness the explosion of digital data and computational power with advanced algorithms to enable collaborative and natural interactions between people and machines.

However, there’s still a lot of confusion within the public and the media regarding what is ML and AI.

People prefer to write AL and ML technologies — and not ML and AI — and the argument goes that the former syncs well with the human mind.

Both the terms are often being used as synonyms and in some cases as discrete, parallel advancements.

In reality, ML is to AI what neurons are to human brain. Let us start with ML.

According to Roberto Iriondo, Editor of Machine Learning Department at Carnegie Mellon University in Pennsylvania, ML is a branch of AI.

As coined by computer scientist and machine learning pioneer Tom M. Mitchell, “ML is the study of computer algorithms that allow computer programmes to automatically improve through experience”.

For instance, if you provide an ML model with songs that you enjoy, along with audio statistics (dance-ability, instrumentality, tempo or genre), it will be able to automate and generate a system to suggest you music that you’ll enjoy in the future, similarly as to what Netflix, Spotify and other companies do.

“In a simple example, if you load an ML programme with a considerable large data-set of X-ray pictures along their description (symptoms etc), it will have the capacity to assist (or perhaps automatise) the data analysis of X-ray pictures later on,” said Iriondo.

The ML model will look at each one of the pictures in the data-set, and find common patterns in pictures that have been labelled with comparable indications.

Shanghai,
Rana el Kaliouby, CEO of the Boston-based artificial intelligence firm Affectiva, demonstrates the company’s facial recognition technology, in Boston, April 23, 2018. VOA

AI, on the other hand, is exceptionally wide in scope and is a system in itself and not just independent data models.

In simpler terms, AI means creating computers that behave in the way humans do.

However, according to Theo van Kraay, Cloud Solution Architect (Advanced Analytics & AI), Customer Success Unit at Microsoft, any attempt to try to define AI is somewhat futile, since we would first have to properly define “intelligence”, a word which conjures a wide variety of connotations.

“Firstly, it is interesting and important to note that the technical difference between what used to be referred to as AI over 20 years ago and traditional computer systems, is close to zero,” says van Kraay.

What AI systems today are doing reflects an important characteristic of human beings which separates us from traditional computer systems – human beings are prediction machines.

Many AI systems today, like human beings, are mostly sophisticated prediction machines.

“The more sophisticated the machine, the more it is able to make accurate predictions based on a complex array of data used to train various (ML) models, and the most sophisticated AI systems of all are able to continually learn from faulty assertions in order to improve the accuracy of their predictions, thus exhibiting something approximating human intelligence,” van Kraay said.

Most ML algorithms are trained on static data sets to produce predictive models, so ML algorithms only facilitate part of the dynamic in the definition of AI.

Fifty years ago, a chess-playing programme was considered a form of AI.

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Today, a chess game would be considered dull and antiquated, due to the fact that it can be found on almost every computer.

“AI today is symbolised with human-AI interaction gadgets like Google Home, Apple Siri and Amazon Alexa or ML-powered video prediction systems that power Netflix, Amazon and YouTube,” says Iriondo.

In contrast to ML, AI is a moving target and its definition changes as its related technological advancements turn out to be further developed.

“Possibly, within a few decades, today’s innovative AI advancements will be considered as dull as flip-phones are to us right now,” quips Iriondo. (IANS)

Next Story

Google AI Can Now Predict Lung Cancer Accurately

The research was published in the journal Nature Medicine

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Google, Main One, russia
A Google logo is displayed at the entrance to the internet based company's offices in Toronto. VOA

A team of Google researchers has used a deep-learning algorithm to detect lung cancer accurately from computed scans.

The work demonstrates the potential for Artificial Intelligence (AI) to increase both accuracy and consistency, which could help accelerate adoption of lung cancer screening worldwide.

Lung cancer is the deadliest of all cancers worldwide — more than breast, prostate, and colorectal cancers combined — and it’s the sixth most common cause of death globally, according to the World Health Organization.

“Using advances in 3D volumetric modeling alongside datasets from our partners (including Northwestern University), we’ve made progress in modeling lung cancer prediction as well as laying the groundwork for future clinical testing,” Shravya Shetty, M.S. Technical Lead at Google explained in a blog post late Monday.

Google researchers created a model that can not only generate the overall lung cancer malignancy prediction (viewed in 3D volume) but also identify subtle malignant tissue in the lungs (lung nodules).

Google on an Android device. Pixabay

In the research, Google AI leveraged 45,856 de-identified chest CT screening cases (some in which cancer was found).

“When using a single CT scan for diagnosis, our model performed on par or better than the six radiologists. We detected five per cent more cancer cases while reducing false-positive exams by more than 11 per cent compared to unassisted radiologists in our study,” said Google.

For an asymptomatic patient with no history of cancer, the AI system reviewed and detected potential lung cancer that had been previously called normal.

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These initial results are encouraging, but further studies will assess the impact and utility in clinical practice, said Google.

The research was published in the journal Nature Medicine. (IANS)