Topic : Making machine learning more useful to high-stakes decision makers

A visual analytics tool helps child welfare specialists understand 

machine-learning predictions that can assist them in screening cases.     

According to the Centers for Disease Control and Prevention, one out of every seven children in the United States had been abused or neglected in the previous year. Every year, child protective services organizations across the country receive a large number of reports of alleged neglect or abuse (about 4.4 million in 2019). Because there are so many cases, some organizations are using machine learning algorithms to assist child welfare specialists in screening cases and deciding which ones to refer for further investigation.

However, these models are useless if the people they're supposed to help don't comprehend or believe the results. Researchers from MIT and other universities have undertaken a study to discover and address machine learning usability issues in child welfare screening. The researchers evaluated how phone screeners analyse situations with and without the use of machine learning predictions in partnership with a Colorado child protection department. They created a visual analytics tool that employs bar graphs to highlight how certain aspects in a case contribute to the anticipated probability of a kid being removed from their family within two years, based on comments from call screeners.

We'll now discuss how to make machine learning more relevant to high-stakes decision-makers.

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Topic : Dexterous robotic hands manipulate thousands of objects with ease

Model-free framework reorients over 2,000 diverse objects with a hand facing 

both upward and downward, in a step toward more human-like manipulation.

A one-year-old baby is already more dexterous than a robot. Sure, machines 

can do more than just pick up and place objects, but we're still a long way 

from mimicking a natural need for exploratory or dexterous manipulation.OpenAI gave it a shot with Dactyl (meaning "finger" from the Greek word "daktylos"), a humanoid robot hand that solved a Rubik's cube with software that's a step toward more broad AI and a departure from the conventional single-task approach. "RGB-Stacking," a vision-based system developed by DeepMind, challenges a robot to learn how to gather and stack things.

Now we'll speak about how dexterous robotic hands can effortlessly manipulate millions of objects.

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Topic : Artificial networks learn to smell like the brain

When asked to classify odors, artificial neural networks 

adopt a structure that closely resembles that of the brain’s olfactory circuitry.

In just a few minutes, a computer model can teach itself to smell using machine learning. 

Researchers discovered that when it does, it creates a neural network that closely resembles 

the olfactory circuits used by animal brains to analyse odours.

From fruit flies to humans, olfactory information is processed in the brain in essentially the same way. However, neuroscientists who trained an artificial neural network to do a simple odour classification job were shocked to see how well it mimicked biology's technique.

Guangyu Robert Yang, an associate scientist at MIT's McGovern Institute for Brain Research who conducted the work as a postdoc at Columbia University, adds, "The method we utilise bears no relation to the actual process of evolution." The similarities between the artificial and biological systems show that the olfactory network in the brain is well-suited to its function.

Now we'll talk about how artificial neural networks can learn to smell like a brain.

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Topic : Avoiding shortcut solutions in artificial intelligence

 

A new method forces a machine learning model to focus on more 

data when learning a task, which leads to more reliable predictions.

You might get to your destination faster if your Uber driver takes a 

shortcut. A machine learning model that takes a shortcut, on the other hand, may fail in unforeseen ways. A shortcut solution in machine learning happens when a model makes a conclusion based on a single feature of a dataset rather than understanding the underlying core of the data, which can lead to erroneous predictions. For example, instead of focusing on the more intricate shapes and patterns of the cows, a model might learn to recognize images of cows by focusing on the green grass that occurs in the photos.

Researchers at MIT have published a new study that looks at the problem of shortcuts in a popular machine-learning method and provides a solution that forces the model to use more data in its decision-making.

Now we'll discuss some recent breakthroughs in artificial intelligence that avoid taking shortcuts.

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Toward speech recognition for uncommon spoken languages

 Reduced complexity of a powerful machine-learning model could 

help level the playing field for automatic voice recognition all throughout the world.

With the emergence of virtual assistants like Siri, automated speech-recognition technology 

has become more ubiquitous, yet many of these systems only work well with the most widely spoken 

of the world's nearly 7,000 languages.

Now we'll talk about the new developments that machine learning has brought to auto-mated voice recognition.

Because these systems are mostly absent for less prevalent languages, millions of individuals who speak them are blocked off from a wide range of speech-based technology, including smart home devices, assistive technologies, and translation services.

Machine learning models that can learn the world's uncommon languages, which lack the enormous volume of transcribed speech required to train algorithms, have recently become possible thanks to recent breakthroughs. However, these methods are frequently too complicated and costly to be broadly used.

Researchers at MIT and other institutions have now devised a simple strategy for reducing the complexity of a sophisticated speech-learning model, allowing it to run more effectively.

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Topic : The Importance of Emotional AI in Business in the 21st Century

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Emotional intelligence is significant in corporate management since it aids in the management of AI 

in the workplace and analyses marketing efforts for a better consumer experience. Several major and 

small businesses are turning to artificial intelligence (AI) to accelerate their growth and outperform 

previous outcomes.

What is the definition of emotional intelligence? To put it another way, emotional intelligence is the 

ability of AI technologies to not only solve but also grasp the causes for the occurrence of complicated 

business challenges and how they affect everyone. The machine is capable of looking after itself.

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TOP 10 AI BREAKTHROUGHS IN HEALTHCARE TO NOTE DOWN

Diagnostics, treatment management, etc are changing, thanks to AI breakthroughs in healthcare:



Over the past two decades, artificial intelligence has created a robust place for itself in human society and our daily lives. Most of us are unaware of the fact that everything starting from our social media feeds to the online shopping experience is powered by AI. Fortunately, healthcare is a major area that is becoming increasingly reliant on technology. Diagnostics, treatment management, drug discovery and production, surgeries, etc are revolutionized by the influence of artificial intelligence. AI sub-technologies like machine learning, natural language processing (NLP), and data science are also helping in identifying healthcare needs and solutions faster with more accuracy. On the other hand, we also fear that these capabilities could surpass human ability and make us worthless. Although this could be eminent in the far future, technology is under human toes for now.


AI-based Cognitive Behavioural Therapy

In today's world, mental health disorders are becoming increasingly widespread. However, dealing with them takes time and is often costly. Unique mental issues like the opioid abuses’ crises are very hectic and need new methods to deliver proper care to patients. Thankfully, AI-based cognitive behavioural therapy can assist.

Included: AI-based Cognitive Behavioural Therapy



Automatic Stroke Detection

Social Media Analysis on Suicidal Thoughts

3D Printed Limbs

Autism Virtual Reality

AI Cancer Detection

AI-enhanced Microscopes

Digital Nurses

Personalized Medical Care

Surgical Robots

The Future of AI in Healthcare



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THE ROLE OF AI, IOT AND DATA ANALYSIS IN CRICKET

 

Data analysis plays a key role in cricket:

We live in a technological age where we are completely surrounded by it. Cricket, as a globally popular sport, could not have been undisturbed by technological advancements for long. The use of technology in cricket has risen steadily throughout time. Third umpires, Hawkeye, and decision review system, as well as power bats and helmet cams, have all been deployed in recent years.

From the comfort of our own homes, we can now watch cricket being played in any region of the world. Our excitement never wanes since technology has made it look so genuine on the screen.

Highlight :Data analysis plays a key role in cricket

Use of AI and IoT in Cricket

SpeculurBatSense

Power Bats

SuperStats

Data Analytics in Cricket

(1. Criclytics,2. CricScience,3. Insights by ESPNcricinfo )

Conclusion : These are just a few examples of how artificial intelligence, internet of things, and big data analytics

are being employed in cricket. In addition to these, Snickometer, Hawk-eye, and Drone Cameras are examples of

modern-day technologies used in cricket.

Technology has revolutionised the entire cricket experience for both spectators and players.

Some regard the increased usage of technology as a threat since it has the potential to undermine

the value of the game. Cricket, on the other hand, is a widely followed sport that cannot be avoided.

Technology and athletics must work together.

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