Thursday, December 12, 2019

Researchers Slam Artificial Intelligence Software That Predicts Emotions

A prominent group of researchers alarmed by the harmful social effects of artificial intelligence called Thursday for a ban on automated analysis of facial expressions in hiring and other major decisions. The AI Now Institute at New York University said action against such software-driven "effect recognition" was its top priority because science doesn't justify the technology's use and there is still time to stop widespread adoption.

The group of professors and other researchers cited as a problematic example the company HireVue, which sells systems for remote video interviews for employers such as Hilton and Unilever. It offers AI to analyze facial movements, tone of voice and speech patterns, and doesn't disclose scores to the job candidates.

Tuesday, December 3, 2019

Subsets of Artificial Intelligence

Machine Learning

Machine learning is a part of AI which provides intelligence to machines with the ability to automatically learn with experiences without being explicitly programmed.
  • It is primarily concerned with the design and development of algorithms that allow the system to learn from historical data.
  • Machine Learning is based on the idea that machines can learn from past data, identify patterns, and make decisions using algorithms.
  • Machine learning algorithms are designed in such a way that they can learn and improve their performance automatically.
  • Machine learning helps in discovering patterns in data.

Natural Language processing

Natural language processing is a subfield of computer science and artificial intelligence. NLP enables a computer system to understand and process human language such as English.
NLP plays an important role in AI as without NLP, AI agent cannot work on human instructions, but with the help of NLP, we can instruct an AI system on our language. Today we are all around AI, and as well as NLP, we can easily ask Siri, Google or Cortana to help us in our language.
Natural language processing application enables a user to communicate with the system in their own words directly.
The Input and output of NLP applications can be in two forms:
  • Speech
  • Text

Deep Learning

Deep learning is a subset of machine learning which provides the ability to machine to perform human-like tasks without human involvement. It provides the ability to an AI agent to mimic the human brain. Deep learning can use both supervised and unsupervised learning to train an AI agent.
  • Deep learning is implemented through neural networks architecture hence also called a deep neural network.
  • Deep learning is the primary technology behind self-driving cars, speech recognition, image recognition, automatic machine translation, etc.
  • The main challenge for deep learning is that it requires lots of data with lots of computational power.

Wednesday, November 27, 2019

Artificial Intelligence Not Seen As A Job-Killer, Yet

Executives don’t see many job cuts ahead of a result of tasks being replaced by AI. Is this a realistic perception?
A recent survey of executives out of IFS tackled issues of AI perception, finding a few business leaders predict worker displacement by AI. Close to half, 46%, predict AI will actually increase headcounts over the coming decade, while 25% predict no changes at all to workforce sizes. Only 18% see AI as a tool for replacing workers.
There are many high hopes for AI — 61% see it boosting the productivity of their workforces. Another 48% also see AI as adding value to their products and services. While a majority anticipate productivity increases from AI, only 29% say such increased productivity will reduce headcounts over the next 10 years. “Respondents did not make the connection between increased productivity and reduced headcount,” the report’s authors suggest.

Monday, November 25, 2019

Cyber security enhanced with AI and ML: Improving data loss prevention

The vast and growing amounts of data being created, collected and used by the enterprise makes the deployment of data security solutions a business imperative. It is essential to implement cybersecurity solutions and practices to prevent data leaks and breaches, but how do businesses stay ahead of the growing sophistication of cyber-attacks?
Predictive technologies, such as artificial intelligence (AI) and machine learning (ML) can enhance traditional data loss prevention (DLP) solutions to greatly reduce the risk of breaches or leaks.

AI can provide critical analysis, and ML uses algorithms to learn from data—both provide a dynamic framework to predict and solve data security problems before they occur. The more data patterns ML analyses, the more processes and self-adjustments can operate based on those learned patterns. This continuous delivery of insights increases in value with the “intelligence” of the technology.

Tuesday, November 19, 2019

What Does Interoperability Mean for the Future of Machine Learning

Interoperability in action: Healthcare

Let’s use healthcare as an example of how interoperable machine learning technology can enhance our lives. Consider high-tech medical procedures like CT scans that automatically generate large volumes of sensor data for a single patient as opposed to health information your doctor manually enters into a proprietary database during a routine check-up. Without a way to quickly and automatically integrate these disparate data types for analysis, there is lost the potential for fast diagnosis of critical illnesses. This has created a demand for optimization across different information models. Current methods and legacy systems simply aren’t friendly in terms of interoperability — but recent developments in machine learning are opening the door for the possibility of stronger, faster translation between information platforms. The result could be vastly improved medical care and optimized research practices.

The role of neural networks

Modeled after the human brain, neural networks are comprised of a set of algorithms that are designed to recognize patterns. They interpret sensory data through a sort of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. According to a 2017 article in MIT News, neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Since that time, the approach has fallen in and out of favor, but today it’s making a serious comeback.

Monday, November 18, 2019

Artificial Intelligence, Machine Learning and Python

Ever since computers were invented, there has been an exponential growth in their ability and potential to perform various tasks. In order to use computers across diverse working domains, humans have developed computer systems while increasing their speed, and reducing size with respect to time.

Artificial Intelligence pursues the stream of developing computers or machines to be as intelligent as humans themselves. In this article, we will scrape the top layer about the concepts of artificial intelligence that will help understand related concepts like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms, etc. Along with this, we will also learn about its implementation in Python.

Sunday, November 10, 2019

Putting Artificial Intelligence to Work

Artificial Intelligence (AI) is one of the hottest technology trends on the planet, but for the average small business owner, it can be terribly intimidating. It’s time to get over that.
While many small and midsized business (SMB) leaders say AI is critical for their business, only one in five are actually doing anything about it, according to a recent Capterra survey.

This should come as no surprise since we all know SMBs don’t tend to deploy new technology until the kinks have been worked out and it becomes more mature. Plus, they have more pressing priorities to deal with, like finding new customers and paying the bills. Right?

But here’s the thing: AI isn’t all that new, and it’s not some temperamental new technology that could come-and-go as quickly as Palm Pilots, Betamax video players and QR Codes. It’s here to stay, finding its way into everything from those personalized shopping suggestions we all get on social media sites to virtual assistants like Amazon’s Alexa and Apple’s Siri. Increasingly, it’s also seeping into SMB operations.