In recent months, there has been a resurgence in the usage of the term “web3”: a nebulous term that (like it or hate it) has suddenly become a banner for much of the innovation happening in the space of blockchain and distributed ledger technology. The term has been around for a while (since 2014 when Gavin Wood originally coined it), but now it seems to have, for better or worse, reached critical mass.
With this has come a large amount of discourse and criticism. The field of discourse has seemingly been almost perfectly split in two: first, the “blockchain likers”, who are a broad group of individuals ranging from dogmatic “moon boy” traders to seasoned cryptographers like Dan Boneh who are working on the core technology associated with the field. On the other side, there are the “blockchain dislikers”: people like Stephen Diehl (who himself actually works on a private blockchain), who consistently produce highly reflexive, reactionary, and dismissive critiques.
These critiques are usually unmoored to anything actually happening in the blockchain space, and unaware of the technological development and cultural vision. This bifurcation is extremely harmful for the health of the emerging technologies: these technologies are not going to go away, so if your only answer is dismissal and anger, you’re actually just ceding the power to decide what these technologies will look like in our world. This pattern of discourse creates a negative-sum feedback loop that does a disservice to all. Read More
Monthly Archives: January 2022
10 Predictions for Web3 and the Cryptoeconomy for 2022
2021 proved to be a breakout year for crypto with BTC price gaining almost 70% yoy, Defi hitting $150B in value locked, and NFTs emerging as a new category. Here’s my view through the crystal ball into 2022 and what it holds for our industry. Read More
Web3 and Blockchain: The ‘access layer,’ decentralized exchanges, and geopolitical finance implications
The buzz-term Web3 is trending quickly. The current reality of Web3 is this— most people have absolutely no idea what it means; large corporations and political institutions are playing Public Relations games with the term; and technical people are having a terrible time defining it.
We are in limbo somewhere between “wtf is Web3?” and “let’s make sure we both have the same definition of Web3…” I’ve been stumped by both the former and the latter.
… There are large corporate, institutional, and political players trying to co-opt the term Web3. Management consulting firms like McKenzie and Deloitte need a new term that can be synonymous with the next wave of Internet innovation consulting services.
… On the surface, the ambiguity and lack of consensus around Web3 are all a bit silly. However, beneath the surface of the non-specific, insecure, and postural Web3 narratives lies a fascinating set of concepts and innovations that are 1) exposing a new ‘access layer’ of distributed Internet-based applications, and 2) growing into an absolutely dissonant threat to the dominant order of our existing monolithic financial and political institutions. Read More
AI that understands speech by looking as well as hearing
People use AI for a wide range of speech recognition and understanding tasks, from enabling smart speakers to developing tools for people who are hard of hearing or who have speech impairments. But oftentimes these speech understanding systems don’t work well in the everyday situations when we need them most: Where multiple people are speaking simultaneously or when there’s lots of background noise. Even sophisticated noise-suppression techniques are often no match for, say, the sound of the ocean during a family beach trip or the background chatter of a bustling street market.
One reason why people can understand speech better than AI in these instances is that we use not just our ears but also our eyes. We might see someone’s mouth moving and intuitively know the voice we’re hearing must be coming from her, for example. That’s why Meta AI is working on new conversational AI systems that can recognize the nuanced correlations between what they see and what they hear in conversation, like we do.
To help us build these more versatile and robust speech recognition tools, we are announcing Audio-Visual Hidden Unit BERT (AV-HuBERT), a state-of-the-art self-supervised framework for understanding speech that learns by both seeing and hearing people speak. It is the first system to jointly model speech and lip movements from unlabeled data — raw video that has not already been transcribed. Read More
CES 2022: Deepbrain humanises AI avatars
DeepBrain AI’s industry-first approach to “humanising” AI assistants provides users with an experience that is familiar, enlightening and approachable. Its video synthesis solutions, a CES 2022 Innovation Awards Winner, leverages the power of Artificial Intelligence to quickly create lifelike human-based AI avatars that inform, solve and guide users through thousands of possible scenarios and real-time interactions.
“Our AI avatars are uniquely developed from real people, using their real voices, physical appearances, gestures and regional dialects,” says the company. “We work in a wide range of industries and our AI solution is used by companies like 7-Eleven, KB Bank, LG HV, and Roche.
DeepBrain AI is one of the top three global companies that possess both deep learning-based video synthesis and voice synthesis source technology. Read More
AI Researchers Portal
Connecting AI researchers to Federal resources that can support their AI work – from grant funding and datasets to computing and testbeds. The National AI Initiative Office’s official site for AI researchers to access datasets, computing resources, and federal grant information. Read More
#artificial-intelligence, #dod, #icNvidia’s upgraded AI art tool turned my obscure squiggles into a masterpiece
It’s incredible, the things we can do with AI nowadays. For artists looking to integrate artificial intelligence into their workflow, there are ever more advanced tools popping up all over the net. One such tool is Nvidia Canvas, which has just been updated with the more powerful GauGAN2 AI, to replace the original GauGAN model, along with loads of new features.
The Nvidia Canvas software is available for free to anyone with an Nvidia RTX graphics card. This is because the software uses the tensor cores present in your GPU to let the AI do it’s job. Read More
Trends in AI Research for the Visual Surveillance of Populations
Since 2014, computer vision models have dramatically improved their performance on benchmarks for image classification, image generation, facial recognition, and other tasks. As these examples show, computer vision researchers aim to solve a wide variety of problems. Yet previous bibliometric studies have examined international output of computer vision research as a whole, without distinguishing among these many research tasks. In principle, analyses of academic research can inform us about the global growth of research and the interests and incentives of each nation’s researchers. But only a small segment of computer vision research may relate to any particular area of interest. In this brief, we focus on “visual surveillance research,” the development of algorithms such as facial recognition that could be used to surveil individuals or groups.
These algorithms are often applied for benign, commercial uses, such as tagging individuals in social media photos. But progress in computer vision could also empower some governments to use surveillance technology for repressive purposes.
Using a dataset of English-language papers published between 2015 and 2019, we applied natural language processing methods to identify the computer vision papers in this corpus and the research tasks they described. Read More
If AI Is Predicting Your Future, Are You Still Free?
AS YOU READ these words, there are likely dozens of algorithms making predictions about you. It was probably an algorithm that determined that you would be exposed to this article because it predicted you would read it. Algorithmic predictions can determine whether you get a loan or a job or an apartment or insurance, and much more.
These predictive analytics are conquering more and more spheres of life. And yet no one has asked your permission to make such forecasts. No governmental agency is supervising them. No one is informing you about the prophecies that determine your fate. Even worse, a search through academic literature for the ethics of prediction shows it is an underexplored field of knowledge. As a society, we haven’t thought through the ethical implications of making predictions about people—beings who are supposed to be infused with agency and free will. Read More
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering
Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers – groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work Read More
#accuracy
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