In this article, I'll show you how I built a system called AI-Memer that generates memes using the latest AI models. I start with a high-level description of the system components before getting into the background of memes and details of the components. I'll then show you how to generate your own memes using the Google Colab, here. After a brief discussion of results and next steps, you can see some sample memes in the appendix. Oh, and I'll show a newly generated meme at the head of each section.

The specialists of the Academy of Artificial Intelligence in Beijing (China) this week presented the most sophisticated natural language processing model in the world, which uses 1.75 trillion parameters to simulate conversational speech, write poems, understand images and even generate recipes, pick up the South China Morning Post newspaper. El WuDao 2.0, which in Chinese means'understanding of natural laws', is a previously trained artificial intelligence model that was developed with the help of more than 100 scientists. It is more powerful than the models of its main competitors: the GPT-3 from the company OpenAI (co-founded by Elon Musk), which was launched with 175,000 million parameters, and the Switch Transformer from Google, which uses 1.6 trillion parameters. The model develops both in Chinese and English acquired skills as you have'studied' 4.9 terabytes of images and texts, including 1.2 terabytes of text in those two languages. WuDao 2.0 already has 22 partners, such as smartphone maker Xiaomi or short video giant Kuaishou.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

One afternoon in late November of last year, Timnit Gebru was sitting on the couch in her San Francisco Bay Area home, crying. Gebru, a researcher at Google, had just clicked out of a last-minute video meeting with an executive named Megan Kacholia, who had issued a jarring command. Gebru was the coleader of a group at the company that studies the social and ethical ramifications of artificial intelligence, and Kacholia had ordered Gebru to retract her latest research paper--or else remove her name from its list of authors, along with those of several other members of her team. The paper in question was, in Gebru's mind, pretty unobjectionable. It surveyed the known pitfalls of so-called large language models, a type of AI software--most famously exemplified by a system called GPT-3--that was stoking excitement in the tech industry.

Pretrained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks and then illustrate the vulnerability of pretrained Transformer-based models against samples that are in-domain but out-of-scope (ID-OOS). We empirically show that pretrained models do not perform well on both ID-OOS examples and general out-of-scope examples, especially on fine-grained few-shot intent detection tasks. To figure out how the models mistakenly classify ID-OOS intents as in-scope intents, we further conduct analysis on confidence scores and the overlapping keywords and provide several prospective directions for future work. We release the relevant resources to facilitate future research.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. In their decades-long chase to create artificial intelligence, computer scientists have designed and developed all kinds of complicated mechanisms and technologies to replicate vision, language, reasoning, motor skills, and other abilities associated with intelligent life. While these efforts have resulted in AI systems that can efficiently solve specific problems in limited environments, they fall short of developing the kind of general intelligence seen in humans and animals. In a new paper submitted to the peer-reviewed Artificial Intelligence journal, scientists at UK-based AI lab DeepMind argue that intelligence and its associated abilities will emerge not from formulating and solving complicated problems but by sticking to a simple but powerful principle: reward maximization. Titled "Reward is Enough," the paper, which is still in pre-proof as of this writing, draws inspiration from studying the evolution of natural intelligence as well as drawing lessons from recent achievements in artificial intelligence.

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Microsoft has been making major investments in very large language models, from the hardware to run them in Azure (which it talks about as an'AI supercomputer') to the DeepSpeed library that speeds up training and running machine-learning models with billions of parameters by spreading them across multiple GPUs. In 2020, Microsoft got an exclusive licence for the powerful (and sometimes controversial) GPT-3 natural language generation model from OpenAI, which uses 175 billion parameters to produce what can look very much like something written by a person. OpenAI has a GPT-3 API that's trained and run on Azure, but it's in private beta and researchers and academics have to apply individually to join a waitlist. Similarly, Microsoft hasn't yet started even a private preview for what it calls the Open AI GPT and Azure Service and the page to sign up for notifications says there is no release date yet. But Microsoft is already using GPT-3 and other natural language generation in its products for features that are much more sophisticated than writing automatic captions for images.

Organizations that contributed to the report include representatives from arXiv, AI Ethics Lab, Black in AI, Bloomberg Government, Burning Glass Technologies, Computing Research Association, Elsevier, Intento, International Federation of Robotics, Joint Research Center, European Commission, LinkedIn, Liquidnet, McKinsey Global Institute, Microsoft Academic Graph, National Institute of Standards and Technology, Nesta, NetBase Quid, PostEra, Queer in AI, State of AI Report, Women in Machine Learning, and many individual contributors. Supporting partners to the report include McKinsey & Company, Google, OpenAI, Genpact, AI21 labs, and PricewaterhouseCoopers.

This week, researchers at the Beijing Academy of Artificial Intelligence (BAAI) announced the release of Wu Dao 2.0, a multimodal AI model capable of generating text indiscernible from human-crafted prose -- and more. Containing 1.75 trillion parameters, the parts of the machine learning model learned from historical training data, Wu Dao 2.0 is 10 times larger than OpenAI's 175-billion-parameter GPT- 3. Wu Dao 2.0 is the latest example of what OpenAI policy director Jack Clark calls model diffusion, or multiple state and private actors developing GPT-3-style AI models. For example, Russia and France are training smaller-scale systems via Sberbank and LightOn's PAGnol, while Korea's Naver Labs is investing in the recently created HyperCLOVA. Clark notes that because these models reflect and magnify the data they're trained on, different countries care about how their own cultures are represented in the models. The Wu Dao 2.0 announcement, then, is part of a general trend of nations asserting their own AI capabilities via training frontier models like GPT-3.