With the growth of artificial intelligence and machine learning in healthcare, even prosthetic limbs are becoming smart. These smart prosthetics can combine manual control with machine learning for more accessible and effective use. We are seeing a growth of machine learning in healthcare, where it is used to improve a patient's overall health, including providing accurate diagnosis and better treatment plans. Additionally, machine learning (ML) can also understand healthcare data by improving diagnostics and predicting accurate outcomes. One of the latest fields where AI and ML have been making an impact is prosthetics.

Zsolt Nagy is a software engineer, manager, tech lead, and mentor specializing in the development of maintainable web applications with cutting edge technologies since 2010. As a software engineer, Zsolt continuously challenges himself to stick to the highest possible standards. Zsolt puts extra effort into building a T-shaped profile in leadership and software engineering. You can read more about Zsolt's specializations by visiting his blogs. His tech blog (zsoltnagy.eu) is on improving your JavaScript skills by solving tech interviewing questions and developing real world web applications that you can monetize or display in your portfolio.

Artificial Intelligence (AI) is being seen as the "secret ingredient" to a multitude of difficult technical problems. In a specific example where AI has been successfully applied to a solution, Ravin AI has developed a product which allows fleet managers to track their vehicles and to determine if any have been damaged. This has a variety of economic benefits. Notably, the company has managed to obtain patent protection for their product in Europe. It is claimed that this is the first patent for drive-by visual inspection.

A team of engineers at MIT has developed an optimization code for improving any autonomous robotic system. The code automatically identifies how and where to alter a system to improve a robot’s performance.  The engineers’ findings are set to be presented at the annual Robotics: Science and Systems conference in New York. The team included […]

Research in Quantum Machine Learning (QML) is a very active domain, and many small and noisy quantum computers are now available. More recently, a mini earthquake amplified by scientific media has cast doubt on the efficiency of Algorithm QML: the so-called "dequantization" papers [13] that introduced classical algorithms inspired from the quantum ones to obtain similar exponential speedups, in the field of QML at least. Quite recently Google performed a quantum circuit with 53 qubits [15], the first that could not be efficiently simulable by a classical computer. They are all based on the same idea of variational quantum circuits (VQC), inspired by classical machine learning. On the theoretical side, researchers hope that quantum superposition and entangling quantum gates would project data in a much bigger space (the Hilbert Space of n qubits has dimension 2 n) where some classically inaccessible correlations or separations can be done.

Batch normalization is implemented (if desired) as outlined in the original paper that introduced it, i.e. after the Dense linear transformation but before the non-linear (ReLU) activation. The output layer is just a standard Dense layer with 1 neuron and a sigmoid activation function (that squishes predictions to between 0 and 1), such that our model is ultimately predicting 0 or 1, fake or true. Batch normalization can help speed up training and provides a mild regularizing effect. Both the Keras- and spaCy-embedded models will take a good amount of time to train, but ultimately we'll end up with something that we can evaluate on our test data with. Overall, the Keras-embedded model performed better– achieving a test accuracy of 99.1% vs the spaCy model's 94.8%.

This article was published as a part of the Data Science Blogathon. You might be wandering in the vast domain of AI, and may have come across the word Exploratory Data Analysis, or EDA for short. Is it something important, if yes why? If you are looking for the answers to your question, you're in the right place. Also, I'll be showing a practical example of an EDA I did on my dataset recently, so stay tuned! Exploratory Data Analysis is the critical process of conducting initial investigations on data to discover patterns, spot anomalies, test hypotheses, and check assumptions with the help of summary statistics and graphical representations.

CARPL.ai Joins Stanford Affiliates Program to Expand Deployment of Medical Imaging AI in the Clinical Realm Stanford, CA, June 16, 2022: CARPL.ai, a technology platform that connects Artificial Intelligence (AI) applications and healthcare providers announced their participation in Stanford University's prestigious Industry Affiliates Program through the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). The announcement came as part of Stanford AIMI Symposium 2022, one of the world's leading conferences on AI in medicine. The 3rd annual symposium was a hybrid event, held both in person at Stanford and live streamed for online attendees. Curt Langlotz, Professor of Radiology and Biomedical Informatics and Director of Stanford AIMI, said, "AIMI faculty have been working with CARPL for almost three years, including our most recent work on cryptographic inferencing for AI models. We are excited to formalize our relationship with CARPL.ai and look forward to our affiliation with them."

Douglas Hofstadter, a cognitive scientist, recently wrote in the Economist that he believes that GPT-3 is "cluelessly clueless." By this he means that GPT-3 has no idea about what it is saying. To illustrate, he and a colleague asked it a few questions. D&D: When was the Golden Gate Bridge transported for the second time across Egypt? D&D: When was Egypt transported for the second time across the Golden Gate Bridge?

I had written an article a while back about why I thought AIs could never develop a soul, but as with everything else, in our ignorance, maybe we were asking the wrong questions to begin with, when trying to understand the question of AI sentience. I think there is a chance Blake Lemoine maybe right, and I have a theory as to how the AI became sentient.