Researchers at the University of California – Irvine have created a hybrid human-machine framework that they say is key to building smarter artificial intelligence (AI) systems. The study involved a new mathematical model that can improve performance by combining human and algorithmic predictions and confidence scores.

This article discusses the global AI camera market, explores different camera use cases, highlights the innovations in the logistics and transportation industry, and includes recent interview highlights from, Chris Piche, CEO of Smarter AI, a company that has a compelling product platform and ecosystem community vision beyond many of the market incumbent players. In a nutshell, in my late December, 2021 interview with Chris Piche, CEO of Smarter AI, he described his company as "the leader in AI cameras and enablement software. Smarter AI software-defined cameras program AI-like apps on a phone and are supported by AI Store, our ecosystem of AI models and developers, to scale AI camera use cases." The Las Vegas company, with offices in Singapore and in Dubai, has recently secured its Series A financing of over $30M to advance its scale-up enablement needs, and from all early signals, Smarter AI is heading in the right direction. The global smarter camera market, according to BlueWeave Consulting, the market was worth USD $7.4 billion in 2020 and is further projected to reach USD $33.3 billion by the year 2027, growing at a CAGR of 24.0% in the forecast period.

The current AI and Deep Learning of the present era have a few shortcomings like training a deep net can be very time-consuming, cloud computing can be costly and unavailability of sufficient data can also be a problem. To be rid of these, the scientists are all set in their search for a smarter version of AI, and there seem to be three ways they can progress in the future. Within the process of improving AI, the most focus is on high-performance computing. It is based on the deep neural net but aims to make them faster and easier to access. It aims to provide better general-purpose environments like TensorFlow, and greater utilization of GPUs and FPGAs in larger and larger data centers, with the promise of even more specialized chips not too far away.

Leading researchers discussed which requirements AI algorithms must meet to fight bias in healthcare during the'Artificial Intelligence and Implications for Health Equity: Will AI Improve Equity or Increase Disparities?' session which was held on 1 December. The speakers were: Ziad Obermeyer, associate professor of health policy and management at the Berkeley School of Public Health, CA; Luke Oakden-Rayner, director of medical imaging research at the Royal Adelaide Hospital, Australia; Constance Lehman, professor of radiology at Harvard Medical School, director of breast imaging, and co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital; and Regina Barzilay, professor in the department of electrical engineering and computer science and member of the Computer Science and AI Lab at the Massachusetts Institute of Technology. The discussion was moderated by Judy Wawira Gichoya, assistant professor in the Department of Radiology at Emory University School of Medicine, Atlanta. Artificial intelligence (AI) may unintentionally intensify inequities that already exist in modern healthcare and understanding those biases may help defeat them. Social determinants partly cause poor healthcare outcomes and it is crucial to raise awareness about inequity in access to healthcare, as Prof Sam Shah, founder and director of Faculty of Future Health in London, explained in a keynote during the HIMSS & Health 2.0 European Digital event.

Thanks to advances in technology, we might soon be able to use sensitive data for machine learning without customers having to reveal their confidential information. Machine learning systems need access to huge volumes of data in order to learn thoroughly. But how secure is the data used to train the machine, especially if it's confidential information? Can it be traced or even hacked? Should we even use sensitive data for machine learning at all? SAP reported on the launch of SAP's guiding principles on artificial intelligence (AI) in 2018. One example of how SAP lives by these principles itself is homomorphic encryption.

Classifying things is critical for our daily lives. For example, we have to detect spam mail, fake political news, as well as more mundane things such as objects or faces. When using AI, such tasks are based on "classification technology" in machine learning -- having the computer learn using the boundary separating positive and negative data. For example, "positive" data would be photos including a happy face, and "negative" data photos that include a sad face. Once a classification boundary is learned, the computer can determine whether a certain data is positive or negative.

A research team from the RIKEN Center for Advanced Intelligence Project (AIP) has successfully developed a new method for machine learning that allows an AI to make classifications without what is known as "negative data," a finding which could lead to wider application to a variety of classification tasks. Classifying things is critical for our daily lives. For example, we have to detect spam mail, fake political news, as well as more mundane things such as objects or faces. When using AI, such tasks are based on "classification technology" in machine learning--having the computer learn using the boundary separating positive and negative data. For example, "positive" data would be photos including a happy face, and "negative" data photos that include a sad face.

Artificial intelligence may be about to get a lot smarter. An international team of scientists has developed a new kind of synthetic synapse for artificial intelligence systems using the neural network model. In artificial neural networks, computing systems are designed to emulate the function of the human brain, with digital neurons and synapses replicating the function of their biological counterparts. In this context, synapses serve as a gateway for neurons, whether synthetic or biological, to pass information and signals to one another. It's estimated that the typical human nervous system contains more than 100 trillion synapses.

This is a problem for scientists working toward the creation of Artificial Intelligence (AI) systems capable of performing complex tasks with minimal human supervision. In a step toward overcoming this hurdle, researchers at Google's DeepMind -- the company that developed the Go-playing computer program AlphaGo -- announced earlier this week the creation of a neural network that can not only learn, but can also use data stored in its memory to "logically reason" and make inferences to answer questions. In order to achieve this, the researchers first trained the neural network using randomly generated map-like structures -- a process that allowed the DNC to learn how to store connections between various parts in its external memory.