If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
With the rise of low cost genome sequencing and AI-enabled medical imaging, there has been substantial interest in precision medicine. In precision medicine, we aim to use data and AI to come up with the best treatment for a disease. While precision medicine has improved outcomes for patients diagnosed with rare diseases and cancers, precision medicine is reactive: the patient has to be sick for precision medicine to be deployed. When we look at healthcare spending and outcomes, there is a tremendous opportunity to improve cost-of-care and quality of living by preventing chronic conditions such as diabetes, heart disease, or substance use disorders. In the United States, 7 out of 10 deaths and 85% of healthcare spending is driven by chronic conditions, and similar trends are found in Europe and Southeast Asia.
The second law of thermodynamics delineates an asymmetry in how physical systems evolve over time, known as the arrow of time. In macroscopic systems, this asymmetry has a clear direction (e.g., one can easily notice if a video showing a system's evolution over time is being played normally or backward). In the microscopic world, however, this direction is not always apparent. In fact, fluctuations in microscopic systems can lead to clear violations of the second law of thermodynamics, causing the arrow of time to become blurry and less defined. As a result, when watching a video of a microscopic process, it can be difficult, if not impossible, to determine whether it is being played normally or backwards.
An urban legend says that a data science task is mainly finished after the development of models. The truth is that a much more important phase follows, often tougher than model development: managing and governing these ready-to-use models to keep your data science project relevant for the long haul. If you're a visual learner, you might prefer tuning into my Open Data Science Conference presentation, First Aid Kit for Data Science: Keeping Machine Learning Alive. In just over 24 minutes, I cover the machine-learning lifecycle, which includes finding the right data, preparing and exploring it and building, registering and reassigning models. I use a fraud detection project as an example.
Welcome to the 4th industrial revolution. This is where automation & artificial intelligence are transforming the way we all live, work, and connect. We've written extensively on the impact of AI in HR, staffing, and recruiting. Just about everything we do is being impacted by artificial intelligence. Farming, retail, healthcare, mental health, construction, IT…pick any industry you like.
In September, Alphabet's DeepMind published a paper in the journal Physical Review Research detailing Fermionic Neural Network (FermiNet), a new neural network architecture that's well-suited to modeling the quantum state of large collections of electrons. The FermiNet, which DeepMind claims is one of the first demonstrations of AI for computing atomic energy, is now available in open source on GitHub -- and ostensibly remains one of the most accurate methods to date. In quantum systems, particles like electrons don't have exact locations. Their positions are instead described by a probability cloud. Representing the state of a quantum system is challenging, because probabilities have to be assigned to possible configurations of electron positions. These are encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons; the wavefunction squared gives the probability of finding the system in that configuration.
Every neural network consists of neurons, synapses, weights, biases, and functions. A neuron or a node of a neural network is a computing unit that receives information, performs simple calculations with it, and passes it further. In a large neural network with many neurons and connections between them, neurons are organized in layers. An input layer receives information, n hidden layers (at least three or more) process it, and an output layer provides some result. If this is the first layer, input output.
The demand for Artificial Intelligence tools and machine learning algorithms have gained importance in various sectors in the past few years. Despite all the benefits offered by AI models, critical threats are endangering their safety and integrity. The AI models and algorithms are trained on large online datasets and third party databases, making them vulnerable to cyberattacks. It is crucial to detect these attacks and mitigate their impact on the system. One type of cyber threat is the neural Trojan attack. The neural Trojans are the malicious inputs that deliberately cause AI models to make mistakes.
Montreal-based YPC Technologies today announced that it has raised a $1.8 million seed round. Led by Hike Ventures and Real Ventures, the funding includes participation from Toyota AI Ventures and Uphill Capital, among others, designed to help the company pilot its kitchen robotics technology. Toyota's funding came as part of the company's "Call of Innovation," which finds it investing in early state AI, robotics and other cutting edge technologies. "At TRI, we're always searching for ways to amplify human ability and help improve quality of life," TRI's Gil Pratt said in a statement. "Through the call for innovation, we got a first-hand look at how startups like YPC Technologies are addressing the needs of people in urban communities, and we're encouraged and excited by their efforts."
While some towns are all but nixing Halloween, Reese's is hoping to bring safe trick-or-treating to neighborhood doorsteps. The company is sending out a remote-controlled robotic door to roll through neighborhoods and dispense king-size Reese's Peanut Butter Cups. When it shows up, kids only have to say "trick or treat" to get their candy. "This Halloween is unlike any other, so we've upped the ante on creativity as a result," Allen Dark, Reese's senior brand manager, said in a statement. "A robotic Reese's dispensing door is just what the world needs right now!" The door works using a remote control from 5,000 feet away.
The integration of AI and IoT into robot systems is expected to significantly expand their application scope. According to a recent study from market research firm Global Market Insights, robotics and automation have emerged over the past few years to become an indispensable part of modern-day manufacturing. A vast majority of manufacturers are integrating robotic systems in production facilities to enhance the production capacity, boost profit margins, and cut operational costs. These trends have created a substantial demand for robotic components, including robot sensors like 3D vision, force-torque, and tactile sensors. It is estimated that the global robot sensor market will be worth more than US$4 billion by 2026. The integration of AI and IoT is expected to expand the application scope of these sensors significantly, particularly across production activities.