Statistical methods, on which all of this is based, have their limitations. That's why domain knowledge must be applied. I watched a conference presentation recently that perfectly illustrated this issue. The speaker said that if you train a deep learning system on a bunch of text and then you ask it the question, "What color are sheep?" it will tell you that sheep are black, and the reason is that even though we know as humans that most sheep are white, it's not something we talk about. It is implicit in our knowledge.

Elon Musk-founded startup OpenAI has discovered multimodal neurons in an Artificial Intelligence system that works just like some neurons work in the human brain. The revelation comes 15 years after the discovery that the human brain possesses multimodal neurons that respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the "Halle Berry" neuron that responds to photographs, sketches, and the text "Halle Berry" -- but not other names. Two months ago, OpenAI announced a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized. It is trained to recognize and people and objects within abstract contexts -- sketches, cartoons, and even statues of the objects.

Data Science, Machine Learning, and Artificial Intelligence are the significant drivers of the fourth industrial revolution. Since data powers all these fields, they are often used interchangeably. However, despite the similarities, Data Science, ML and AI are different from each other. Data Science is a multidisciplinary field with a focus on the use of data to derive insights. A good data scientist must possess a wide range of skills, including programming, mathematics, and domain knowledge of the desired field of application.

IMAGE: SMU Assistant Professor Sun Qianru says highly diverse training data is critical to ensure the machine sees a wide range of examples and counterexamples that cancel out spurious patterns. SMU Office of Research and Tech Transfer - Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyses data and makes predictions, and relies heavily on human supervision.

Python is one of the significant and widely used programming languages of the world. It is an open source programming. And it is mainly known for being high level, object oriented and the most powerful language. It is one of the takeaway Data Science languages. Thus, data scientists use this language for performing data analytics.

Hyperparameter tuning is one of the fundamental steps in the machine learning routine. Also known as hyperparameter optimisation, the method entails searching for the best configuration of hyperparameters to enable optimal performance. Machine learning algorithms require user-defined inputs to achieve a balance between accuracy and generalisability. This process is known as hyperparameter tuning. There are various tools and approaches available to tune hyperparameters.

Being a data scientist by profession and a part-time crypto trader by passion, I have been very interested in creating a Deep Learning model that could help me predict Bitcoin price. This article is based on the experimentation I did to create such a model. Long short term memory, or more popularly known as LSTM's, is a type of Recurrent Neural network that helps the model learn long-term sequences in the data set. Since my focus here is more on their usage, if you are interested in knowing more details about what LSTM's are and how they work, you can check out this great article that goes in-depth to explain all that. I imported the data onto my local drive and read it as a CSV using pandas. For this model I created fields to track the hour of the day and the weekday.

Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks. As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyzes data and makes predictions, and relies heavily on human supervision. SMU Assistant Professor of Information Systems, Sun Qianru, likens training a small-scale AI model to teaching a young kid to recognize objects in his surroundings.

Deep learning is that form of AI which excels in incorporating the human brain that ultimately aids in better decision-making capabilities. There are numerous applications that rely on deep learning. One such application that garnered attention from everyone across is its incorporation in AI chips. Jeff Dean, an American computer scientist and also Google's brain director had mentioned how Google would be using artificial intelligence to advance its internal development of custom chips about a year ago. This would ultimately pave the way for accelerating its software.

In precision crop protection, (target-orientated) object detection in image processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection drones) to the right place to apply the pesticide. Unnecessary application of non-target areas could be avoided. Deep learning algorithms dominantly use in modern computer vision tasks which require high computing time, memory footprint, and power consumption. Based on the Edge Artificial Intelligence, we investigate the main three paths that lead to dealing with this problem, including hardware accelerators, efficient algorithms, and model compression. Finally, we integrate them and propose a solution based on a light deep neural network (DNN), called Ag-YOLO, which can make the crop protection UAV have the ability to target detection and autonomous operation. This solution is restricted in size, cost, flexible, fast, and energy-effective. The hardware is only 18 grams in weight and 1.5 watts in energy consumption, and the developed DNN model needs only 838 kilobytes of disc space. We tested the developed hardware and software in comparison to the tiny version of the state-of-art YOLOv3 framework, known as YOLOv3-Tiny to detect individual palm in a plantation. An average F1 score of 0.9205 at the speed of 36.5 frames per second (in comparison to similar accuracy at 18 frames per second and 8.66 megabytes of the YOLOv3-Tiny algorithm) was reached. This developed detection system is easily plugged into any machines already purchased as long as the machines have USB ports and run Linux Operating System.