Mediaboss Marketing

From taking home gold in the Delaware Valley Science Fair competition his sophomore year to having one of his most time-consuming and difficult research papers rejected at a regional competition Yash Prabhu turns his failure into a success story.

Yash Prabhu starts the school year strong with one of his research papers being published at a conference called IEEE. IEEE is an organization dedicated to assisting humanity through enhancing technology. The organization sponsors over 2,000 events yearly and organizes top-tier conferences with published papers being recognized by academia and industries. Prabhu recently had a paper published called A CNN Based Automated Stuttering Identification System, where he uses machine learning to detect different types of stuttering in audio segments.

Stuttering affects a lot of people around the world and its a surprisingly ignored issue. Some people who stutter receive speech therapy but a lot of people dont. Stuttering can damage the quality of life for people. It makes it harder to speak and harder to interact with people. You could get bullied, teachers could get frustrated speaking to you [and] it gets worse when youre older because you have to give meetings and presentations, explained senior Yash Prabhu.

The goal for his research paper revolves around his wish to make low-cost automation available to people worldwide, mainly in developing countries such as India where resources are scarce.

I am trying to design a model that can classify stuttering. By classifying stuttering it can help keep data. In an area where theres a lack of speech pathologists like in India, this model can have a big impact by keeping diagnostics on stuttering and also helping speech pathologists do a better and faster job so they can treat more people, Prabhu said.

The model Prabhu used is called a machine learning model. Machine learning creates functions to help make predictions.

I found a data set provided by Apple called SEP-28k and this data set [consists] of many examples of stuttering. I chose 5 different speech disfluencies and I trained my model to detect the speech disfluencies, Prabhu added.

Prabhu took the initiative to explore a data set that had not been extensively researched leading to hardships.

I emailed professors, I called doctors, I asked for access to data sets. Nothing really [worked] out because it was hard to find someone who could help me get access to data. A lot of doctors and professors are busy, they dont have time to talk to you, Prabhu said disappointed.

Nonetheless, his chances of success improved when Dr. Naeem Seliya, a professor at the University of Wisconsin Eau Claire, agreed to assist him.

I emailed him my credentials and asked him if I could work with him. He is actually a stutterer and he told me about a device he uses for stuttering. From there this idea was born in my mind. I can use my machine learning skills to have an impact in this area. At that point, I had no clue how to do it. I had absolutely no clue, I just knew I wanted to. For months I did research. I tried finding different ways to do it, but I failed a lot of times, Prabhu explained.

The difficulties he encountered while working with audio data resulted in poor models that produced faulty results.

When I submitted this to the local science fair it didnt go through which was a disappointment. I thought this was the most difficult project I have ever done to this day, Prabhu said disheartened.

He reflected back on his Sophomore year, where he designed a Covid-19 screener using machine learning and hardware. The project was inspired by the pandemic and received gold at the Delaware Valley Science Fair. However, his recent paper was rejected by the local science fair.

Being rejected by the local science fair was a big bruisebut I think it was a blessing in disguise because I got to make this whole paper. This paper was born from the science fair project failing. I made my models better, I trained them with more data, [and] I ended up submitting them to the conference, Prabhu added.

In the future, Prabhu plans to attend a four year university and continue doing research while immersing himself in robotics, engineering, and programming.

Continued here:
Yash Prabhu celebrating victory after publication of research paper - Knight Crier

Cybernetics will drive the future of robotics by empowering them with human intelligence

Robotics Industry is constantly rising in this automation world. According to reports, the Indian industrial robotics market is predicted to grow at a CAGR of 13.3% between 2019-2024. With its rising industry applications and productivity benefits, the study of cybernetics is likely to be a vital element in the advancement of robotics.

The craving for gadgets or machines that can keep up with the challenges of the present world and largely function in simpler and smarter ways is evident. Automation and autonomy have offered this by producing and delivering products and services that contain the least amount of human intervention, making certain jobs more convenient than ever before even when information is incomplete and uncertain. The appearance of new service robots and their wide evolution into new applications has further facilitated the world of automation. Due to the dynamic nature of robotics, numerous application sectors are now using robotics to perform predetermined tasks and enhance human efforts in both physical and analytic ways. Robotics has enhanced task efficiency, dependability, and quality, all of which were earlier, products of a laborious procedure. Being a critical component of automation, robotics is currently used in an ever-growing variety of fields, like manufacturing, transportation, healthcare & medical care, utilities, defence, facilities, operations, and more recently, information technology. Here Cybernetics enters as a primary element as robots need to be advanced.

Cybernetics is a study of science that focuses on developing technologies that act or think like humans by researching how electrical devices or machines and the human brain function to enhance the value of the job to be performed. Cybernetics is the best workaround physical embodiment of Artificial Intelligence (AI), Machine Learning (ML), and predictive analysis and control, investigating underlying systems/structures, possibilities, and limitations of complex mechanisms, including robotics, and generating an autonomous environment that uses minimal to no human interaction. AI and cybernetics are two dissimilar perspectives on intelligent systems or systems that may act to achieve an aim. Making computers imitate intelligent behavior using pre-stored world representations is the primary goal of AI. In general, cybernetics tells us how systems control themselves and can take actions autonomously based on environmental signals even when the information is minimal and subject to significant uncertainty or noise. These systems go beyond simple computation; they can also control biological (body temperature regulation), mechanical (engine speed regulation), social (managing a huge workforce), and economic (controlling a national economy) systems.

Every cybernetic systems aim is to be set up so that its operations are linked in a variety of input-output system configurations which are normally driven with reference control signs. This is achieved by processing feedback-based automatic closed-loop control systems that can decide which behaviors should be changed, which actions should be tracked, how to compare the actions to the reference, and how to adapt the application behaviors in the most effective way. In natural cybernetic systems, this regulatory mechanism generates or organizes by itself with the help of self-learning. On the other hand, artificial cybernetic systems behave or are influenced by human-implemented automatic control systems. Essential elements of cybernetic systems are sensors, the controller, actuators and the system to be controlled.

Cybernetics in robotics systems main objective is to use AI and machine learning in the sense-plan-act paradigm normally used to develop robots so they can operate productively in real-world scenarios. Developing a robot to understand and differentiate complex situations every day is highly demanding and getting the situation awareness correctly identified is crucial to ensuring the desired reference control signal can be identified for implementation. This can make sure an industrial robot recognizes and picks up the correct item for the next stage of the manufacturing process from a selection of parts to ensure the requests of the human to be served a variety of beverages will get the correct drink. Sensors and sensor systems that are perfectly calibrated are necessary for ensuring the situation awareness is achieved perfectly and in real-time using AI-based models which can be learned and applied in various situations such as driverless cars, medical robots, automated manufacturing, and home care robots.

Share This Article Do the sharing thingy

About Author More info about author

See the original post:
Cybernetics is the Only Way Robots Can Achieve Human Intelligence - Analytics Insight

If youve heard the term machine learning, but arent quite sure what it means, then youve come to the right place. Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being specifically programmed to do so. Basically, machine learning (MI) and artificial intelligence (AI) are helping businesses by improving customer service, reducing errors, managing automation and much more. Why do you need to know all of this? Well, for all of you out there looking to boost your income and career opportunities, you should consider this handy bundle that will give you the basics in machine learning.

The Premium Machine Learning Artificial Intelligence Super Bundle offers you 79 hours, 12 courses and 438 training on Python, data science, analysis and tons more. Start by learning the fundamentals of Python, and dont worry its not all theory. Youll be getting some serious hands-on training. Learn the powerful tools used in data science and machine learning and get certified. Create deep learning algorithms in Python, master the importance of deep learning for Python, harness the power of the H2O framework for machine learning with R, create your very own image detection app and so much more.

With each course rating 4+ stars or higher, you know you are in good hands to learn the fundamentals of machine learning and artificial intelligence. Need further convincing? In the words of one 5-star reviewer, The Premium Machine Learning Artificial Intelligence Super Bundle is amazing, lot of information on Machine Learning and Artificial Intelligence. Great quality on videos. Must have this bundle!!!

If youve been looking for a way out of your 9-5 nightmare, but just havent had the opportunity, now is your chance. This learning bundle comes with lifetime access, which means you can learn whenever or wherever you need. And, you can feel free to revisit material any time.

Join the 531 people enrolled today, and begin your journey towards a more lucrative career.

Get The Premium Machine Learning Artificial Intelligence Super Bundle for $36.99 (reg. $2,388), a discount of 98 per cent.

Prices subject to change.

This article includes sponsored material. Read our transparency policy for more information.

See the original post here:
Be On The Cutting-Edge Of Tech With This Top-Rated Learning Bundle - IFLScience

BELLEVUE, Wash. and WATKINSVILLE, Ga., Sept. 13, 2022 (GLOBE NEWSWIRE) -- AI Dynamics and PETTIGREW Medical announced today the formation of a joint venture, with the working name mAIcode, designed to accelerate the automation of medical record coding by applying advanced machine learning. AI Dynamics is an organization founded on the belief that everyone should have access to the power of artificial intelligence (AI) to change the world. PETTIGREW Medical is a pioneer in providing revenue cycle management services and has expanded into a diversified and accredited industry leader on a global scale.

The joint venture with AI Dynamics will enable us to create an automated coding solution that provides an order of magnitude improvement in productivity, efficiency and accuracy, while also reducing costs, said David Young, president and chief financial officer, PETTIGREW Medical. Once the joint venture is fully operational, we look forward to serving a larger percentage of the $18 billion annual medical coding market, which is growing at a compounded annual growth rate (CAGR) of eight percent.

Today, the medical coding market is highly complex, with more than 68,000 diagnostic codes and over 10,000 Current Procedural Terminology (CPT) codes. Current coding approaches are expensive, with the median salary of a medical coder in the U.S being more than $50,000. The typical coder can code at most a few hundred medical records a day, with each record costing between $2 to $20 to code. The volume of content to code is enormous and growing, meaning costs will grow as well.

Unlike other AI coding companies that are focused primarily on the cloud, we have developed mAIcode to be equally efficient for customers that want to manage their data on premise or in a more secure environment. We are also orienting the solution to audit-level accuracy, backed by the NeoPulse Platform, said Rajeev Dutt, founder and CEO of AI Dynamics. Our solution relies on multiple deep learning models built on the NeoPulse Platform, that provides the joint venture with the unique ability to continuously improve its own medical coding capabilities based on experience the AI solution is learning continuously.

The solution is built on a SaaS model that can be run in the cloud or at the customers location. At the core of the solution is AI Dynamics NeoPulse Framework, which will enable customers to manage their entire AI workflow and infrastructure from one place. NeoPulse enables lower cost and faster design and deployment of AI solutions. Customers can adopt the solution to their own medical chart formats. It also features clear explainability, which is necessary for audits, increasing confidence in decisions, and providing peace of mind to clients and auditors. It incorporates federated learning data privacy technology; data never leaves the data owners firewall but the solution enables data users to generate insights from the data, ensuring all parties remain in compliance with HIPAA, U.S state data privacy regulations and data residency requirements. As mAIcode learns from customer use, it will quickly outperform manual solutions.

About AI Dynamics:AI Dynamics aims to make artificial intelligence (AI) accessible to organizations of all sizes. The company's NeoPulse Framework is an intuitive development and management platform for AI, which enables companies to develop and implement deep neural networks and other machine learning models that can improve key performance metrics. The company's team brings decades of experience in the fields of machine learning and artificial intelligence from leading companies and research organizations. For more information, please visit aidynamics.com.

About Pettigrew Medical:PETTIGREW Medical specializes in billing, coding, accounts receivable management and contact center solutions for healthcare billing companies, hospitals, private practices and insurers with large central business office operations. Since 1989, PETTIGREW has provided superior, aggressive, and compliant services to our clients. PETTIGREW is continuously seeking ways to make the experience of running a facility or group easier on owners and medical directors, and of making their practice's information easily accessible. For more information, please visit pettigrewmedical.com.

Media Contact:Madi Oliv / Valeria CarrilloUPRAISE Marketing + PR for AI Dynamics and PETTIGREW Medicalaidynamics@upraisepr.com

Originally posted here:
AI Dynamics and PETTIGREW Medical Announce Joint Venture that Applies Advanced Machine Learning to Accelerate Automation of Medical Record Coding -...

Looking for a fun introduction to AI with a sense of humor? Look no further than Making Friends with machine learning (MFML), a lovable free YouTube course designed with everyone in mind. Yes,everyone. If youre reading this, the course is for you!

Image by Randall Munroe,xkcd.comCC.

Short form videos:Most of the videos below are 15 minutes long, which means you get to upgrade your knowledge in bite-sized, well, bites. Tasty bites! Dive right in at the beginning or scroll down to find the topic youd like to learn more about.

Long form videos:For those who prefer to learn in 12 hour feasts, the course is also available as 4 longer installmentshere.

Making Friends with machine learningwas an internal-only Google course specially created to inspire beginners and amuse experts.* Today, it is available to everyone!

The course is designed to give you the tools you need for effective participation in machine learning for solving business problems and for being a good citizen in an increasingly AI-fueled world. MFML is perfect for all humans; it focuses on conceptual understanding (rather than the mathematical and programming details) and guides you through the ideas that form the basis of successful approaches to machine learning. It has something for everyone!

After completing this course, you will:

I was simply blown away by the quality of her presentation. This was a 6-hour(!) tour de force; through every minute of it, Cassie was clear, funny, energetic, approachable, insightful and informative.Hal Ableson, Professor of Computer Science at MIT

I cannot emphasize enough how valuable it was that this course was targeted towards a general audience. Human resources specialist

Fantastic class, plus it is hilarious! Software engineer

I now feel more confident in my understanding of ML Loved it. Communications manager

More useful than any of the courses I took in university on this stuff. Reliability engineer

I loved how she structured the course, knowing the content, and navigating this full-day course without getting us bored. So I learned two things in this lesson. 1) Machine learning, and 2) Presentation skills. Executive

Great Stuff: I would recommend it. ML Research Scientist

always interesting and keeps my attention. Senior leader, Engineering

well structured, clear, pitched in at the right level for people like me and full of useful visuals and stories to help me understand and remember. I learnt a ton. Senior leader, Sales

View original post here:
Everything Youve Ever Wanted to Know About Machine Learning - KDnuggets