Testing for pathogens is a critical component of maintaining public health and safety. Having a method to rapidly and reliably test for harmful germs is essential for diagnosing diseases, maintaining clean drinking water, regulating food safety, conducting scientific research, and other important functions of modern society. In recent research, scientists from University of California, Los Angeles (UCLA), have demonstrated that artificial intelligence (AI) can detect harmful bacteria from a water sample up to 12 hours faster than the current gold-standard Environmental Protection Agency (EPA) methods. In a new study published yesterday in Light: Science and Applications, the researchers created a time-lapse imaging platform that uses two separate deep neural networks (DNNs) for the detection and classification of bacteria. The team tested the high-throughput bacterial colony growth detection and classification system using water suspensions with added coliform bacteria of E. coli (including chlorine-stressed E. coli), K. pneumoniae and K. aerogenes, grown on chromogenic agar as the culture medium.
In today's digital era, artificial intelligence (AI) is at the core of every automatic or self-sustaining system available in the marketplace. Be it the robotics workforce used in the manufacturing industry and logistics or the tools employed in the fields like healthcare, agriculture and automobile, AI is ubiquitous. All industry leaders across verticals are focused heavily on AI and advance solutions involving Deep Learning and Neural Networks. BPU Holdings is a global company, headquartered in Korea that pioneers in the development of Artificial Emotional Intelligence (AEI). The mission of the company is to generate the most advanced, secure usable, and innovative Artificial Emotional Intelligence technology in the world.
The practice to include Artificial Intelligence in industry application is skyrocketing for a decade now. It is evident since, AI and its constituent applications Machine Learning, computer vision, facial analysis, autonomous vehicles, deep learning form the pillars of modern digital empowerment. The ability to learn the data it is trained up to understand the binary, quantum computation of the world, and make decisions derived from its insights makes AI unique than earlier technologies. Leaders believe that possessing AI-based technologies equate to future industry successes. From healthcare, research, finance, logistics to military, law enforcement department AI holds the key to massive competitive edge and up-gradation with monetary benefits too.
The human brain is an incredibly efficient source of intelligence. Earlier this month, OpenAI announced it had built the biggest AI model in history. This astonishingly large model, known as GPT-3, is an impressive technical achievement. Yet it highlights a troubling and harmful trend in the field of artificial intelligence--one that has not gotten enough mainstream attention. Modern AI models consume a massive amount of energy, and these energy requirements are growing at a breathtaking rate.
I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.
With the emergence of incredibly powerful machine learning technologies, such as Deepfakes and Generative Neural Networks, it is all the easier now to spread false information. In this article, we will briefly introduce deepfakes and generative neural networks, as well as a few ways to spot AI-generated content and protect yourself against misinformation. I have many elderly relatives and some middle-aged relatives that just aren't well-versed with technology. Some of these people believe nearly anything they read, or at least believe it enough to share it on social media. While that doesn't sound so bad, it depends on what you are sharing.
The Open Invention Network (OIN) is the largest patent non-aggression community in history. Its chief job is protecting Linux and open-source friendly companies from patent attacks. Now, Baidu, the largest Chinese language search engine and one of the world's leading artificial intelligence (AI) firms in the world, has joined OIN. This move makes perfect sense AI is almost entirely driven by open-source programs such as TensorFlow, Keras, and Theano. So, even before this intellectual property law move, Baidu has been an active, global open-source AI supporter.
A number of AI researchers, data scientists, sociologists, and historians have written an open letter to end the publishing of research that claims artificial intelligence or facial recognition can predict whether a person is likely to be a criminal. The letter, signed by over 1000 experts, argues that data generated by the criminal justice system cannot be used to "identify criminals" or predict behaviour. Historical court and arrest data reflect the policies and practises of the criminal justice system and are therefore biased, the experts say. "These data reflect who police choose to arrest, how judges choose to rule, and which people are granted longer or more lenient sentences," the letter reads. Moreover, by continuing these studies, "'criminality' operates as a proxy for race due to racially discriminatory practices in law enforcement and criminal justice, research of this nature creates dangerous feedback loops" the letter says.
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Consider the animal in the following image. If you recognize it, a quick series of neuron activations in your brain will link its image to its name and other information you know about it (habitat, size, diet, lifespan, etc…). But if like me, you've never seen this animal before, your mind is now racing through your repertoire of animal species, comparing tails, ears, paws, noses, snouts, and everything else to determine which bucket this odd creature belongs to. Your biological neural network is reprocessing your past experience to deal with a novel situation. Our brains, honed through millions of years of evolution, are very efficient processing machines, sorting out the ton of information we receive through our sensory inputs, associating known items with their respective categories. That picture, by the way, is an Indian civet, an endangered species that has nothing to do with cats, dogs, and rodents.
When discussing the threats of artificial intelligence, the first thing that comes to mind are images of Skynet, The Matrix, and the robot apocalypse. The runner up is technological unemployment, the vision of a foreseeable future in which AI algorithms take over all jobs and push humans into a struggle for meaningless survival in a world where human labor is no longer needed. Whether any or both of those threats are real is hotly debated among scientists and thought leaders. But AI algorithms also pose more imminent threats that exist today, in ways that are less conspicuous and hardly understood. In her book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, mathematician Cathy O'Neil explores how blindly trusting algorithms to make sensitive decisions can harm many people who are on the receiving end of those decisions.