This is 4R's sixth consecutive year participating in the show and will present its latest Merchant Analytics approach. NextPoint provides an exclusive annual event which offers retailers and solution providers an experience uncommon from other industry events and trade shows. Mark Garland, Executive Vice President Sales, Marketing & Solutions, said, "It is hard to believe this will be our sixth year presenting at NextPoint. The last five years at NextPoint have provided excellent networking opportunities. We are looking forward to sharing how 4R positions retailers to earn more profit from their inventory with proven success stories."
While buzzwords such as predictive maintenance, artificial intelligence, digital twin and augmented reality have promised to enable the fabled digital transformation of manufacturing, when it comes to Industry 4.0, most practical applications start and end with machine connectivity. And when it comes to driving value, look no further than answering these questions; "What's happening?" Simply put, most manufacturers are unable to see what's actually happening on the shop floor in real time because their machines are not connected to any sort of data collection or data visualization system. This inability to both see and use data to drive continuous improvement leads to massive inefficiencies that affect every component of a company's operations, from the shop floor all the way to the C-Suite. That said, as the excitement around the opportunity presented by AI continues to grow, we conducted an interview with our very own Lou Zhang, Chief Data Scientist at MachineMetrics, so he could give us his perspective on where AI lands within the Analytics Journey and its relationship to technologies such as machine monitoring and data collection.
OpenAI has come up with a new robot capable of solving a Rubik's Cube with a single hand. The AI-based company trained neural networks in simulation using reinforcement learning to make this achievement possible. The company has been working on this project since May 2017 and has now achieved its goal marking this as a milestone towards its progress in the field of AI. The time taken by the robotic hand varies depending on how the cube is shuffled but on average, it takes about four minutes to solve the puzzle. However, it is worth noting that this is not the first-ever robot that managed to solve the Rubik's cube.
We first formulate the MDP for our problem, M S,? QWeb solves the above problem using deep Q network(DQN) to generate Q values for each state and for each atomic action. The training process is almost the same as traditional DQN with the help of reward augmentation and some curriculum learning approaches, which we will discuss later. But for now let's first focus on the architecture of QWeb, which is essentially the most fruitful part of this algorithm. Encoding user instructions: As we've seen in the preliminaries, a user instruction consists of a list of fields, i.e.,key-value pairs K, V .
Alphabet (Google) subsidiary Wing has become the first company in the United States to deliver packages by drone. In Christiansburg, the small Virginia town chosen as Wing's test location, the 22,000 residents can order products normally shipped by FedEx, medicine from Walgreens and a selection of candy from a local business -- all of which will arrive via drone. Wing, which already operates in two Australian cities as well as Helsinki, announced in a statement that the first drone-powered deliveries had taken place Friday afternoon in Christiansburg, "paving the way for the most advanced drone delivery service in the nation". One family used the Wing app to order Tylenol, cough drops, Vitamin C tablets, bottled water and tissues, the statement said. An older resident ordered a birthday present for his wife.
One of the most promising applications of deep learning is image analysis (as part of computer vision), e.g. for image segmentation or classification. Whereas segmentation yields a probability distribution (also known as mask) for each class per pixel (i.e. each pixel belongs to 1 of K classes), classification does so for the whole image (i.e. each image belongs to 1 of K classes). Software solutions can be encountered nearly everywhere nowadays, for example in medical image analysis. In clinical research, where novel medications are tested, sometimes it is of interest if a drug can change the condition of a tissue, e.g. Medical images are created by imaging techniques such as medical ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), or even regular microscopes.
On Monday 8th April 2019, the European Commission's High-Level Expert Group on Artificial Intelligence (AI HLEG) revealed ethics guidelines aimed at forming best practices for creating "trustworthy AI." In fact, many argue this issue of trust in the AI system is one of the main hurdles the technology must overcome for more widespread implementation. A Forbes survey found that nearly 42% of respondents "could not cite a single example of AI that they trust"; in another survey, when respondents were asked what emotion best described their feeling towards AI, "Interested" was the most common response (45%), but it was closely followed by "concerned" (40.5%), "skeptical" (40.1%), "unsure" (39.1%), and "suspicious" (29.8%). The Commission's guidelines are a new roadmap for businesses to align their AI systems. While these guidelines are not policy, it is easy to imagine that they will serve as the building blocks for such regulations.
Since our recent release of Transformers (previously known as pytorch-pretrained-BERT and pytorch-transformers), we've been working on a comparison between the implementation of our models in PyTorch and in TensorFlow. We've released a detailed report where we benchmark each of the architectures hosted on our repository (BERT, GPT-2, DistilBERT, ...) in PyTorch with and without TorchScript, and in TensorFlow with and without XLA. We benchmark them for inference and the results are visible in the following spreadsheet. We would love to hear your thoughts on the process.
Trust me, I have no intention of trusting autonomous vehicle braking. One of the terms we see pop up in almost every technical vector is autonomous vehicles. As with 5G, the autonomous vehicle landscape is fraught with hype. That has even spilled over to the consumer marketing arena with tons of ads for automobiles showing hands-off braking, lane navigation, self-parking, and more. Depending upon with whom one speaks, autonomous vehicles are anywhere from level 3 to level 5. Of course, the only one who believes we are at level 5 is Elon Musk, with his claims for Teslas.
A big challenge in collecting and analyzing intelligence has always been scalability. Good, actionable intelligence takes expertise to develop. Let's say you're a government trying to gather information on a foreign power. You'll need experts who speak the language, know the culture well enough to blend in, have the right skill sets, and are sympathetic to your goals. Finding enough experts who meet those criteria will be difficult -- and even then, it still might not be enough to get regular, actionable intelligence.