As of today, FastAPI is the most popular web framework for building microservices with python 3.6 versions. By deploying machine learning models as microservice-based architecture, we make code components re-usable, highly maintained, ease of testing, and of-course the quick response time. FastAPI is built over ASGI (Asynchronous Server Gateway Interface) instead of flask's WSGI (Web Server Gateway Interface). This is the reason it is faster as compared to flask-based APIs. It has a data validation system that can detect any invalid data type at the runtime and returns the reason for bad inputs to the user in the JSON format only which frees developers from managing this exception explicitly.

Dr. LeCun tweets that "ML systems are biased when data is biased." This can be interpreted in multiple ways, with one interpretation being that data is the only factor that matters, and another being that data is the main problem in this particular case. Dr. Gebru replies in an exasperated way noting that the first possible interpretation is incorrect and that experts such as her say this often. Implicitly, it's clear that this exasperation must be partially because this is a common and harmful misconception experts such as Dr. Gebru have to fight against.

The recent explosive interest on transformers has suggested their potential to become powerful "universal" models for computer vision tasks, such as classification, detection, and segmentation. However, how further transformers can go - are they ready to take some more notoriously difficult vision tasks, e.g., generative adversarial networks (GANs)?.. Driven by that curiosity, we conduct the first pilot study in building a GAN \textbf{completely free of convolutions}, using only pure transformer-based architectures. Our vanilla GAN architecture, dubbed \textbf{TransGAN}, consists of a memory-friendly transformer-based generator that progressively increases feature resolution while decreasing embedding dimension, and a patch-level discriminator that is also transformer-based. We then demonstrate TransGAN to notably benefit from data augmentations (more than standard GANs), a multi-task co-training strategy for the generator, and a locally initialized self-attention that emphasizes the neighborhood smoothness of natural images. Equipped with those findings, TransGAN can effectively scale up with bigger models and high-resolution image datasets.

Marketing is evolving day by day. The need to upgrade your marketing is more now than ever. AI is now ruling every industry out there and marketing is no exception to it. Though it's a new trend and most of the organizations and marketers are not aware of this trend completely, a fair number of organizations have started implementing it already. So I thought to give an overview of AI powered Marketing.

In front of a packed room of MIT students and alumni, Vivienne Ming is holding forth in a style all her own. "Embrace cyborgs," she calls out, as she clicks to a slide that raises eyebrows even in this tech-smitten crowd. Fifteen to 25 years from now, cognitive neuroprosthetics will fundamentally change the definition of what it means to be human." She's referring to the work that interests her most these days, as cofounder of machine learning company Socos and a visiting scholar at UC Berkeley's Center for Theoretical Neuroscience. If you're curious, the answer is unambiguously yes.") But the talk has covered a lot more than this, as Ming has touched on many initiatives and startups she's been involved with, all solving problems at the intersection of advanced technology, learning, and labor economics.

Washington [US], February 28 (ANI): In order to accurately identify patients with a mix of psychotic and depressive symptoms, researchers from the University of Birmingham recently developed a way of using machine learning to do so. The findings of the research were published in the journal'Schizophrenia Bulletin'. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a'primary' illness, but with secondary symptoms. Making an accurate diagnosis is a big challenge for clinicians and diagnoses often do not accurately reflect the complexity of individual experience or indeed neurobiology.

Although nothing really changes but the date, a new year fills everyone with the hope of starting things afresh. If you add in a bit of planning, some well-envisioned goals, and a learning roadmap, you'll have a great recipe for a year full of growth. This post intends to strengthen your plan by providing you with a learning framework, resources, and project ideas to help you build a solid portfolio of work showcasing expertise in data science. Just a note: I've prepared this roadmap based on my personal experience in data science. This is not the be-all and end-all learning plan.

The combination of human and machine learning, wherever they complement one another, has a lot of potential applications in citizen science. Several projects have already integrated both forms of learning to perform data-centred tasks (Willi et al. 2019; Sullivan et al. 2018). While the term artificial intelligence (AI) is generally used to refer to any kind of machine or algorithm able to observe the environment, learn, and make decisions, the term machine learning (ML) has been defined'as a subfield of artificial intelligence that includes software able to recognize patterns, make predictions, and apply newly discovered patterns to situations that were not included or covered by their initial design' (Popenici and Kerr 2017, p. 2). ML algorithms are currently the most widely used and applied, for example, in image and speech recognition, fraud detection, and reproducing human abilities in playing Go or driving cars. In scientific research, they find many applications in different fields such as biology, astronomy, and social sciences, just to mention a few (Jordan and Mitchell 2015).

Machine learning (ML) is to train a machine so that it can make decisions for us. This can be achieved by expert system or machine learning. Expert system is a computer system that emulates the decision-making ability of a human expert. Expert system are also known as Rule Based Systems. It emulates how a human makes a decision.

Living things achieved perfection through natural selection. A swarm could do many things, which its individuals could not. Swarms do not just adapt to their environment but can construct suitable habitats for their own advantages. A constructive understanding of the intelligence of living things is productive in biology and engineering. The aim of this joint symposium DARS-SWARM2021 is the construction of a bridge between biologists and engineers who are interested in the intelligence of living things and the creation of a new academic field by integrating biology and engineering.