TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

We all agree that artificial intelligence (AI) has the power to drive development and even out global inequalities. Because it can process vast amounts of data rapidly, AI is ensuring more and more people in developing countries have access to microfinance, healthcare and remote-learning opportunities. AI helps make climate change mitigation more efficient, and can help deliver housing at a quarter of the usual costs when combined with 3D printing technology. It is easy to see how it could be a game-changer in the rapidly urbanising developing world. But AI's potential to help us achieve the Sustainable Development Goals, and to reduce global poverty is far from being realised.

In one of computer science's more meta moments, professor Chelsea Finn created an AI algorithm to evaluate the coding projects of her students. The AI model reads and analyzes code, spot flaws and gives feedback to the students. Computers learning about learning--it's so meta that Finn calls it "meta learning." Finn says the field should forgo training AI for highly specific tasks in favor of training it to look at a diversity of problems to divine the common structure among those problems. The result is AI able to see a problem it has not encountered before and call upon all that previous experience to solve it.

Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. Millions of students are already starting to benefit from the use of these technologies, but millions more around the world are not. If this trend continues, the first delivery of AI in Education could be greater educational inequality, along with a global misallocation of educational resources motivated by the current technological determinism narrative. In this paper, we focus on speculating and posing questions around the future of AI in Education, with the aim of starting the pressing conversation that would set the right foundations for the new generation of education that is permeated by technology. This paper starts by synthesising how AI might change how we learn and teach, focusing specifically on the case of personalised learning companions, and then move to discuss some socio-technical features that will be crucial for avoiding the perils of these AI systems worldwide (and perhaps ensuring their success). This paper also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We also emphasise the need for collectively designing human-centered, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as support new emerging pedagogies. Finally, we ask what would it take for this educational revolution to provide egalitarian and empowering access to education, beyond any political, cultural, language, geographical and learning ability barriers.

Emotion Recognition in Conversations (ERC) is an important and active research problem. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some external stimuli evokes a change. There is a continuous ebb and flow of emotions in a conversation. Inspired by this observation, we propose a multimodal ERC model and augment it with an emotion-shift component. The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications), to improve emotion recognition. We experiment with different variants of the model, and results show that the inclusion of emotion shift signal helps the model to outperform existing multimodal models for ERC and hence showing the state-of-the-art performance on MOSEI and IEMOCAP datasets.

The university is part of a new Southeastern Conference Artificial Intelligence Consortium, which is focused on ensuring that students graduate with the AI and data science knowledge to compete in an increasingly high-tech workplace. Believed to be the first athletics conference collaboration to focus on artificial intelligence for workforce development, the SEC Artificial Intelligence Consortium is designed to grow opportunities in the fast‐changing fields of AI and data science, which are expected to be foundational for the future of industry, education and research. "This consortium acknowledges the rapid advances and increased applications of AI and data technology in all sectors of society, and it ensures our students are prepared to prosper in a workforce in which AI is expected to play an increasingly important role," said Jere W. Morehead, president of the Southeastern Conference and the University of Georgia. "With this effort, SEC institutions are also answering the call from local, state and federal leaders who recognize the importance of enhanced training and workforce development to retain U.S. global competitiveness." Through the SEC Artificial Intelligence Consortium, member universities will share educational resources, such as curricular materials, certificate and degree program structures, and online presentations of seminars and courses; promote faculty, staff, and student workshops and academic conferences; and seek joint partnerships with industry.

Bill Henderson once advised me not to use the term "industrialization" to describe changes in the legal profession to attorneys. It offends us, and we disengage. But I titled this field note "industrial evolution" because we must embrace industrialization as a necessary and valuable part of our transformation that will elevate the value of our profession in a digital age. This post is part of a series that reflects my legal industry learning journey, building upon my career journey (080), professional evolution (143), focus on knowledge work (159), and future practice design theory (210). This installment examines the changes happening now that require us to evolve to serve a civilization experiencing exponential change powered by the fourth industrial revolution, and how we might get there faster, together. See Erik Brynjolfsson & Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (2016) (cognitive automation will produce creative destruction). This post was drafted proximal to the College of Law Practice Management's 2021 Futures Conference, which offered expert commentary on the information work industrialization megatrend and strongly influenced the thesis presented here: that we are experiencing accelerating change as a secular trend. As discussed below, my tentative solution is, in part, to invert the law's traditional pyramid structure. None of this is likely to make much sense, however, without first describing the set of challenges before us.

Amazon Web Services (AWS) this week announced a public preview of SageMaker Studio Lab. AWS also announced the Artificial Intelligence & Machine Learning Scholarship. The new US $10 million program will prepare underrepresented and underserved students globally for careers in Machine Learning. The announcements came during this week's AWS re:Invent event. AWS hopes SageMaker Studio Lab will attract developers, academics and data scientists to learn and experiment with Machine Learning (ML).

Artificial Intelligence (AI) revolution is here! The purpose of this course is to provide you with knowledge of key aspects of modern Artificial Intelligence applications in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets. The course covers many new topics and applications such as Emotion AI, Explainable AI, Creative AI, and applications of AI in Healthcare, Business, and Finance. Here's a summary of the projects that we will be covering:

We all agree that artificial intelligence (AI) has the power to drive development and even out global inequalities. Because it can process vast amounts of data rapidly, AI is ensuring more and more people in developing countries have access to microfinance, healthcare and remote-learning opportunities. AI helps make climate change mitigation more efficient, and can help deliver housing at a quarter of the usual costs when combined with 3D printing technology. It is easy to see how it could be a game-changer in the rapidly urbanising developing world. But AI's potential to help us achieve the Sustainable Development Goals, and to reduce global poverty is far from being realised.