Model-agnostic meta-learning (MAML) effectively meta-learns an initialization of model parameters for few-shot learning where all learning problems share the same format of model parameters -- congruous meta-learning. We extend MAML to incongruous meta-learning where different yet related few-shot learning problems may not share any model parameters. A Learned Fine Tuner (LFT) is used to replace hand-designed optimizers such as SGD for the task-specific fine-tuning. Here, MAML instead meta-learns the parameters of this LFT across incongruous tasks leveraging the learning-to-optimize (L2O) framework such that models fine-tuned with LFT (even from random initializations) adapt quickly to new tasks. As novel contributions, we show that the use of LFT within MAML (i) offers the capability to tackle few-shot learning tasks by meta-learning across incongruous yet related problems (e.g., classification over images of different sizes and model architectures), and (ii) can efficiently work with first-order and derivative-free few-shot learning problems. Theoretically, we quantify the difference between LFT (for MAML) and L2O. Empirically, we demonstrate the effectiveness of LFT through both synthetic and real problems and a novel application of generating universal adversarial attacks across different image sources in the few-shot learning regime.

The collaboration between data scientists from the Wyss Institute's Predictive BioAnalytics Initiative and synthetic biologists in Wyss Core Faculty member Jim Collins' lab at MIT was created to apply the computational power of machine learning, neural networks, and other algorithmic architectures to complex problems in biology that have so far defied resolution. As a proving ground for their approach, the two teams focused on a specific class of engineered RNA molecules: toehold switches, which are folded into a hairpin-like shape in their "off" state. When a complementary RNA strand binds to a "trigger" sequence trailing from one end of the hairpin, the toehold switch unfolds into its "on" state and exposes sequences that were previously hidden within the hairpin, allowing ribosomes to bind to and translate a downstream gene into protein molecules. This precise control over the expression of genes in response to the presence of a given molecule makes toehold switches very powerful components for sensing substances in the environment, detecting disease, and other purposes.

This is a brand new Machine Learning and Data Science course just launched this year and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 350,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).

For some, it is self-evident to use AI in their business and digitalization in the company. For others, it seems more like a trend. And yet we can observe how more and more AI platforms are in use. Nevertheless, and perhaps precisely because of this, it is challenging to determine how to implement this technology. If misused, misunderstood, or wrongly applied, artificial intelligence can damage businesses.

One major influence on my view of AI, was spending 2 years in a synthetic biology lab. I came to appreciate how much intelligence is contained in living systems (even as simple as a cell). And attending Michael Levin's NeurIPS 2018 keynote underscored that the key to intelligence is not just in the brain -- that it is everywhere in the body, with even a single cell being autonomous and competent. This motivated my desire to leverage open-ended, biological intelligence in AI -- shifting my view away from just "intelligence", and more towards "artificial life". When I was a child, I played with a brand of educational toys called Discovery Toys.

Beyond the initial shock of mass unemployment, a growing hunger crisis, and widespread disruption of our routines, COVID-19 has begun to transform the business and employment landscape at a deeper level. It essentially hit the fast-forward button on pre-existing trends toward automation, robotization, and artificial intelligence (AI) as companies race to replace now-risky human contact with digital capabilities and services which endanger jobs in warehousing, manufacturing, and retail. Other large-employment industries like travel and tourism that could absorb these losses will likely struggle to survive for years to come. Our recovery hinges on getting tens of millions of unemployed workers reskilled and back into jobs -- and educating our children to be future-ready. We learned from the last crisis that education is key to a robust economic recovery.

We are planning for AI to help universities recruit, oversee adaptive learning and drive intelligent chat boxes, but there is much more ahead in the near …

By definition, diversity implies active and inclusive participation. When it comes to Artificial Intelligence Systems (AIS), it's even more critical to establish strategies and partnerships that can foster genuine and holistic inclusion regarding design for these important technologies. Since 2016, City AI has become the world's leading non-profit connecting the AI community globally and supporting local AI ecosystems to develop further. AI provides one of the greatest opportunities for humanity. Yet the access to its expertise and resources is limited.

Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course takes on Machine Learning and Statistical theory and teaches you to use it in solving 20 real-world Business problems. Data Scientist is the buzz of the 21st century for good reason! The tech revolution is just starting and Data Science is at the forefront. As a result, "Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review & Glassdoor.

Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications. Using two crowdsourcing approaches, crowdsourcing task distribution and reverse captcha generation, to construct knowledge graph in the field of teaching system. Generating a complete hierarchical knowledge graph of the teaching domain by nodes of school, student, teacher, course, knowledge point and exercise type. The knowledge graph constructed in a crowdsourcing manner requires many users to participate collaboratively with fully consideration of teachers' guidance and users' mobilization issues. Based on the three subgraphs of knowledge graph, prominent teacher, student learning situation and suitable learning route could be visualized. Personalized exercises recommendation model is used to formulate the personalized exercise by algorithm based on the knowledge graph. Collaborative creation model is developed to realize the crowdsourcing construction mechanism. Though unfamiliarity with the learning mode of knowledge graph and learners' less attention to the knowledge structure, system based on Crowdsourcing Knowledge Graph can still get high acceptance around students and teachers