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We will be discussing the Gaussian Mixture Model. A basic prerequisite to this blog is that one must know about the Gaussian distribution. The Gaussian is also called the normal distribution by statistics people. However, the GMM is called the GMM because in this scenario the G stands for Gaussian and it's not called a normal mixture model. Let's first start with a basic intuition of what a Gaussian mixture is by considering only a single Gaussian we can begin with a typical example.

Facebook's Chief Technology Officer Mike Schroepfer believes the pessimism surrounding technology in the world today is "founded on real concerns of the negative impacts of technology." In an interview on Tuesday with the president of the Oxford Union, the Oxford University debating society, Schroepfer said that in some cases "we haven't really always done the homework upfront" and thought about what a "bad actor" might do with a particular product before releasing it. A Facebook spokesperson told CNBC on Wednesday that he was talking about the tech industry as a whole as opposed to Facebook specifically. The social media giant has been widely criticized for a range issues including the spread of hate speech and misinformation, influencing elections, being addictive, and failing to keep children safe on its platform. "The thing that's often true of new technologies and advancements is they often have very clear, acute examples where things change or are disruptive," said Schroepfer. "It may be a loss of jobs or a new form of scammer abuse. It's something that's really easy to understand as'bad.' And then they have very generalized improvements in the quality of life. So if I say I've increased the GDP overall by 3%, I've made everyone slightly more prosperous, but it's harder to weigh against these very acute, specific harms."

Machine learners, deep learning practitioners, and data scientists are continually looking for the edge on their performance-oriented devices. That's why we looked at over 2,000 laptops to bring you what we consider the best laptops for your projects on machine learning, deep learning, and data science. We will continuously update this resource with powerful and more performant laptops for every budget as technology continues to evolve to bring you the best suggestions for your machine learning, data science, and deep learning projects and adventures. Our mailbox is full of emails from AI enthusiasts asking us for the best laptops for AI projects. That's why we decided to make this list.

Google Colab is a project from Google Research, a free, Jupyter based environment that allows us to create Jupyter [programming] notebooks to write and execute Python [1](and other Python-based third-party tools and machine learning frameworks such as Pandas, PyTorch, Tensorflow, Keras, Monk, OpenCV, and others) in a web browser. A programming notebook is a type of shell or kernel in the form of a word processor, where we can write and execute code. The data required for processing in Google Colab can be mounted into Google Drive or imported from any source on the internet. Project Jupyter is an open-source software organization that develops and supports Jupyter notebooks for interactive computing [4]. Google Colab requires no configuration to get started and provides free access to GPUs.

In recent years, countless computer scientists worldwide have been developing deep neural network-based models that can predict people's emotions based on their facial expressions. Most of the models developed so far, however, merely detect primary emotional states such as anger, happiness and sadness, rather than more subtle aspects of human emotion. Past psychology research, on the other hand, has delineated numerous dimensions of emotion, for instance, introducing measures such as valence (i.e., how positive an emotional display is) and arousal (i.e., how calm or excited someone is while expressing an emotion). While estimating valence and arousal simply by looking at people's faces is easy for most humans, it can be challenging for machines. Researchers at Samsung AI and Imperial College London have recently developed a deep-neural-network-based system that can estimate emotional valence and arousal with high levels of accuracy simply by analyzing images of human faces taken in everyday settings.

A business school takes a multidisciplinary approach to teaching students about the critical role of ethics in the deployment of artificial intelligence. San Francisco has a long history of discovery--from the Gold Rush to the tech revolution. The city also has a history of embracing people-centered social justice. It makes sense, then, that faculty at San Francisco State University (SFSU) would want to combine the two as we explore the implications of one of the next frontiers of discovery: artificial intelligence. I have found that business schools largely discuss AI within other topic areas such as product development or marketing.

The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold. The first is to promote research in the area of AI, and the second is to promote the responsible use of these types of technology. The result was a 35th AAAI Conference on Artificial Intelligence (AAAI-21) schedule that broadens the possibilities of AI and is heavily reflective of a pivotal time in AI research when experts are asking bigger questions about how best to responsibly develop, deploy, and integrate the technology. Microsoft and its researchers have been pursuing and helping to foster responsible AI for years--developing innovative AI ethics checklists and fairness assessment tools like Fairlearn, establishing the Aether Committee to make principle-based recommendations, and laying out guidelines for human-AI interaction, to name only a few of the milestones in this area. As a natural extension, researchers from Microsoft are presenting papers at this year's AAAI that show the wide net they're casting when it comes to developing responsible AI and using it for applications that do good.

"Talent and technology are the keys to unlocking our future in this industry-- finding ways for tech to come in and do a better job than people can in roles people have traditionally done," said MDC Partners global president Julia Hammond in explaining AI's value to her holding company. "The challenge with that is it's completely contradictory to the agency model, which has been built around people, so there's been a reluctance to build out AI and machine learning. We're actively pursuing it, in how we resource, how we scale and how we serve clients." Progress is being made elsewhere to find a happy middle ground. Last week, GroupM agency Wavemaker went public with its AI-driven media planning tool, Maximize, which the company claims is generating plans faster and more effectively than human planning teams alone. "It's a question of complexity of the problem solved.

After Amazon's three-week re:Invent conference, companies building AI applications may have the impression that AWS is the only game in town. Amazon announced improvements to SageMaker, its machine learning (ML) workflow service, and to Edge Manager -- improving AWS' ML capabilities on the edge at a time when serving the edge is considered increasingly critical for enterprises. Moreover, the company touted big customers like Lyft and Intuit. But Mohammed Farooq believes there is a better alternative to the Amazon hegemon: an open AI platform that doesn't have any hooks back to the Amazon cloud. Until earlier this year, Farooq led IBM's Hybrid multi-cloud strategy, but he recently left to join the enterprise AI company Hypergiant.