<h2 dir="ltr" id="isPasted">Challenge Overview</h2>The Build2gether Challenge was designed to address gaps in assistive technologies, focusing on enhancing the daily lives of individuals with disabilities. Participants were called upon to develop cutting-edge solutions across three specific themes: gaming and travelling for people with mobility impairments, and swimming for people with visual impairments.&nbsp;<h2 dir="ltr">Spotlight on the Ultimate Winner: Bob Hammell</h2>Among the various outstanding submissions, Bob Hammell's projects stood out, earning him the Inclusive Innovation Challenge Ultimate Award. Hammell presented three distinct projects, each targeting a different theme of the challenge:<ul><li dir="ltr">Underwater Sound Beacons for Visually Impaired Swimmers: An innovative solution utilizing audio cues to help visually impaired swimmers navigate safely in pools. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAzODYxMDEzOTgyLTE3MDM4NjEwMTM5ODIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="451" class="fr-fic fr-dii">Image credit: Bob Hammell</li><li dir="ltr">Voice-Enabled Video Game Controller: A controller designed for gamers with limited mobility, allowing voice commands to serve as game inputs. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAzODYxMDEzODEwLTE3MDM4NjEwMTM4MTAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="451" class="fr-fic fr-dii">Image credit: Bob Hammell</li><li dir="ltr">Interactive Display for Finding Wheelchair-Accessible Places: A touch-enabled display aiding mobility-impaired individuals in locating wheelchair-accessible facilities while travelling. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAzODYxMDEzOTAxLTE3MDM4NjEwMTM5MDEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="602" height="451" class="fr-fic fr-dii">Image credit: Bob Hammell</li></ul>Each project demonstrated a deep understanding of the challenges faced by individuals with disabilities and offered practical, innovative solutions.<h2 dir="ltr">More Winners and Awards</h2>In addition to Hammell's achievements, the Build2gether Challenge recognized numerous other contributions across its varied themes. These awards highlighted the diverse range of innovations, each contributing significantly to the field of assistive technologies. <a href="https://www.hackster.io/contests/buildtogether" target="_blank" class="button-main-action">Learn more</a><h2 dir="ltr">Discover More</h2>The Build2gether Inclusive Innovation Challenge has set a new benchmark in technological advancements for disability support. For a complete overview of the challenge, the winners, and their groundbreaking projects, interested individuals are encouraged to <a href="https://www.hackster.io/contests/buildtogether" target="_blank">visit this page&nbsp;</a>for detailed information and insights into these remarkable innovations.

The Build2gether Inclusive Innovation Challenge has successfully concluded with the announcement of its winners. This challenge represented a collaborative effort to push the boundaries of innovation in creating solutions for people with disabilities.


Build2gether Inclusive Innovation Challenge: Winners Announced

This article provides a detailed overview of the Modbus TCP protocol fundamentals, communication, data representation, security, daily life applications, and integration.

Modbus TCP: A Comprehensive Guide to the Protocol and Its Applications

The language model ChatGPT is widely known by people in the tech community and the general public. Through a chatbot, one can interact with these language models directly without needing any programming skills. Instead of using code, the user can input commands and questions with natural language.Sometimes this interaction works well, and the desired result appears on-screen. However, at other times the language model does not understand the command, and the generated output can turn out unexpectedly or even unsatisfactory. What people usually do as a response is that they follow up with another query. ChatGPT will then try to correct its mistakes and adapt its answer. This way of using a language model is messy and random, and it can take quite some time to get the preferred outcome.To solve this problem, ETH researchers now developed a new programming language and open-source platform called LMQL (Language Model Query Language). Utilizing LMQL allows a user to interact with large language models like ChatGPT in a more elevated and controlled way. This programming language enables a novel way of programming and is a new form of computer-human interaction because the user can directly talk to and instruct the computer.LMQL is the first language that combines natural and programming languages&rsquo; power to interact with these large language models. For simple queries, it is sufficient to guide ChatGPT using natural language. However, for more complex and specific tasks, such as creating a database or analyzing data, it is essential to instruct the language model precisely. Therefore, the formalism of programming languages is needed to guide the language model with formal constructs to ensure the user gets the desired output. Martin Vechev, Professor of Computer Science and one of the creators, clarifies: &ldquo;Essentially, it is a much more concise way to get what you want. Decreasing the necessary exchanges with the language model also reduces the costs of interacting with the model, which can be quite expensive. Using LMQL increases the chances of getting the desired output. It sometimes even makes it possible to get a result you would have never gotten otherwise because you can formulate your query more accurately.&rdquo;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1682690936421-image.imageformat.1286.1170780518.png" style="width: 766px;" class="fr-fic fr-dib">LMQL allows the user to combine natural and programming languages when formulating a query. (Image: SRI Lab / ETH Zurich)&nbsp;<h2 id="isPasted">A key advantage</h2>The datasets that large language models are trained with and based on are so massive that the user cannot control and comprehend what happens internally within the model. Because of that, these models can sometimes produce unexpected or controversial outputs. According to the researchers, one of the substantial problems for people using these large language models is that they cannot understand why a certain result was produced and how to prevent it.LMQL allows its user to express safety constraints which can help guide the model in the right direction and try to steer it away from unwanted or unexpected outputs. &ldquo;Using LMQL, you can restrict your language model to strictly follow a specific framework you designed. This allows you to better control how the language model behaves. Of course, full guaranteed prevention of bad behaviour is still very hard to achieve, but LMQL is one step in this direction&rdquo;, explains Luca Beurer-Kellner, one of the researchers. For example, LMQL allows the user to exclude specific words or prevents the model from going in certain directions of reasoning.<h2>Transparency is key</h2>Many companies develop their large language models behind closed doors. Due to that, the large language models become intransparent, and their reasoning behind a particular output is incomprehensible to the user. According to the researchers, to counterbalance this, academia must produce open-source tools like LMQL that are transparent, accessible, and adaptable for people. Marc Fischer, who also developed the new programming language, further declares: &ldquo;I think for both a technical and non-technical crowd, it is crucial to have this open-source to see what is going on rather than LMQL being a magical black box. Especially since the research on language models is moving at an insane pace, it is crucial that LMQL simultaneously offers transparency and enables fast development.&rdquo;<h2>A helpful tool even for less experienced users</h2>LMQL is a declarative, SQL-like language from a syntactic point of view. Therefore, it is a very accessible language requiring less expertise to achieve the desired results. The new programming language can also function as an innovative tool for researchers of various disciplines. &ldquo;If you are not too invested in coding or may not have the time to code because it is not a core part of your work, then LMQL makes it much more accessible to interact with large language models in a precise, yet easy way&rdquo;, explains Beurer-Kellner.Additionally, it can function as a helpful base for advanced users and an expert community because you can add different programming constructs to the natural language query. Technical programmer users can use LMQL as a building block and build their own programs on top to interact with large language models.A vast interest and interdisciplinary community is already beginning to form around the new programming language. The researchers declare LMQL a long-term project and plan several follow-ups and papers. They were also accepted to present their work in June at the ACM PLDI, one of the top international conferences on programming language design and implementation.

The interaction with large language models is cheaper and more reliable through LMQL. (Image: SRI Lab / ETH Zurich)

Researchers from ETH Zurich created a new open-​source platform and programming language called LMQL. This programming language makes it easier, cheaper, and safer to interact with large language models like ChatGPT.

A new form of human-computer interaction

In this episode, we talk about how perfectly tuning the curiosity of an AI results in more efficient and robust models. &nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/06e95808-1e8a-4962-a0ee-f1bd3d7bf323?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe><hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>. &nbsp; &nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1670339698469-Mouser-sponsor-1-a.jpg" style="width: 766px;" class="fr-fic fr-dib"><hr><h3 id="isPasted">EPISODE NOTES</h3>(5:20) -&nbsp;<a href="https://www.wevolver.com/article/ensuring-ai-works-with-the-right-dose-of-curiosity" target="_blank">Ensuring AI Works With The Right Dose of Curiosity</a>This episode was brought to you by Mouser, our favorite place to get electronic components for any project. Click <a href="https://resources.mouser.com/artificial-intelligence/ai-can-code-now" target="_blank">HERE</a> to learn more about the copilot and GPT AI models discussed in this episode!<hr>As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">&nbsp;shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" rel="nofollow" target="_blank">&nbsp;Apple&nbsp;</a>podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank">&nbsp;Spotify</a>, and most of your favorite podcast platforms!

In this episode, we talk about how perfectly tuning the curiosity of an AI results in more efficient and robust models. 

Podcast: More Curious AI To Save The World

Article #5 of the Enabling IoT Series. MEMS microphones can deliver high-quality sound in an ultra-small form-factor. 


High SNR microphones are transforming laptops into all-round communication hubs

This article was discussed in our <a data-saferedirecturl="https://www.google.com/url?q=https://www.wevolver.com/profile/the.next.byte&source=gmail&ust=1670426207448000&usg=AOvVaw1x-b-kkAmH54ptZfmv08qg" href="https://www.wevolver.com/profile/the.next.byte" id="isPasted" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/06e95808-1e8a-4962-a0ee-f1bd3d7bf323?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The full article will continue below.<hr>This article was first published on <a href="https://www.csail.mit.edu/news/ensuring-ai-works-right-dose-curiosity" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>It&rsquo;s a dilemma as old as time. Friday night has rolled around, and you&rsquo;re trying to pick a restaurant for dinner. Should you visit your most beloved watering hole or try a new establishment, in the hopes of discovering something superior? Potentially, but that curiosity comes with a risk: If you explore the new option, the food could be worse. On the flip side, if you stick with what you know works well, you won&#39;t grow out of your narrow pathway. &nbsp;Curiosity drives artificial intelligence to explore the world, now in boundless use cases &mdash; autonomous navigation, robotic decision-making, optimizing health outcomes, and more. Machines, in some cases, use &ldquo;reinforcement learning&rdquo; to accomplish a goal, where an AI agent iteratively learns from being rewarded for good behavior and punished for bad. Just like the dilemma faced by humans in selecting a restaurant, these agents also struggle with balancing the time spent discovering better actions (exploration) and the time spent taking actions that led to high rewards in the past (exploitation). Too much curiosity can distract the agent from making good decisions, while too little means the agent will never discover good decisions.In the pursuit of making AI agents with just the right dose of curiosity, researchers from MIT&rsquo;s Improbable AI Laboratory and Computer Science and Artificial Intelligence Laboratory (CSAIL) created an algorithm that overcomes the problem of AI being too &ldquo;curious&rdquo; and getting distracted by a given task. Their algorithm automatically increases curiosity when it&#39;s needed, and suppresses it if the agent gets enough supervision from the environment to know what to do.When tested on over 60 video games, the algorithm was able to succeed at both hard and easy exploration tasks, where previous algorithms have only been able to tackle only a hard or easy domain alone. With this method, AI agents use fewer data for learning decision-making rules that maximize incentives. &nbsp;&ldquo;If you master the exploration-exploitation trade-off well, you can learn the right decision-making rules faster &mdash; and anything less will require lots of data, which could mean suboptimal medical treatments, lesser profits for websites, and robots that don&#39;t learn to do the right thing,&rdquo; says Pulkit Agrawal, an assistant professor of electrical engineering and computer science (EECS) at MIT, director of the Improbable AI Lab, and CSAIL affiliate who supervised the research. &ldquo;Imagine a website trying to figure out the design or layout of its content that will maximize sales. If one doesn&rsquo;t perform exploration-exploitation well, converging to the right website design or the right website layout will take a long time, which means profit loss. Or in a health care setting, like with Covid-19, there may be a sequence of decisions that need to be made to treat a patient, and if you want to use decision-making algorithms, they need to learn quickly and efficiently &mdash; you don&#39;t want a suboptimal solution when treating a large number of patients. We hope that this work will apply to real-world problems of that nature.&rdquo;&nbsp;It&rsquo;s hard to encompass the nuances of curiosity&rsquo;s psychological underpinnings; the underlying neural correlates of challenge-seeking behavior are a poorly understood phenomenon. Attempts to categorize the behavior have spanned studies that dived deeply into studying our impulses, deprivation sensitivities, and social and stress tolerances.&nbsp;With reinforcement learning, this process is &ldquo;pruned&rdquo; emotionally and stripped down to the bare bones, but it&rsquo;s complicated on the technical side. Essentially, the agent should only be curious when there&rsquo;s not enough supervision available to try out different things, and if there is supervision, it must adjust curiosity and lower it.&nbsp;Since a large subset of gaming is little agents running around fantastical environments looking for rewards and performing a long sequence of actions to achieve some goal, it seemed like the logical test bed for the researchers&rsquo; algorithm. In experiments, researchers divided games like &ldquo;Mario Kart&rdquo; and &ldquo;Montezuma&rsquo;s Revenge&rdquo; into two different buckets: one where supervision was sparse, meaning the agent had less guidance, which were considered &ldquo;hard&rdquo; exploration games, and a second where supervision was more dense, or the &ldquo;easy&rdquo; exploration games.&nbsp;Suppose in &ldquo;Mario Kart,&rdquo; for example, you only remove all rewards so you don&rsquo;t know when an enemy eliminates you. You&rsquo;re not given any reward when you collect a coin or jump over pipes. The agent is only told in the end how well it did. This would be a case of sparse supervision. Algorithms that incentivize curiosity do really well in this scenario.&nbsp;But now, suppose the agent is provided dense supervision &mdash; a reward for jumping over pipes, collecting coins, and eliminating enemies. Here, an algorithm without curiosity performs really well because it gets rewarded often. But if you instead take the algorithm that also uses curiosity, it learns slowly. This is because the curious agent might attempt to run fast in different ways, dance around, go to every part of the game screen &mdash; things that are interesting, but do not help the agent succeed at the game. The team&rsquo;s algorithm, however, consistently performed well, irrespective of what environment it was in.&nbsp;Future work might involve circling back to the exploration that&rsquo;s delighted and plagued psychologists for years: an appropriate metric for curiosity &mdash; no one really knows the right way to mathematically define curiosity.&nbsp;&ldquo;Getting consistent good performance on a novel problem is extremely challenging &mdash; so by improving exploration algorithms, we can save your effort on tuning an algorithm for your problems of interest, says Zhang-Wei Hong, an EECS PhD student, CSAIL affiliate, and co-lead author along with Eric Chen &rsquo;20, MEng &rsquo;21 on a new paper about the work. &ldquo;We need curiosity to solve extremely challenging problems, but on some problems it can hurt performance. We propose an algorithm that removes the burden of tuning the balance of exploration and exploitation. Previously what took, for instance, a week to successfully solve the problem, with this new algorithm, we can get satisfactory results in a few hours.&rdquo;&ldquo;One of the greatest challenges for current AI and cognitive science is how to balance exploration and exploitation &mdash; the search for information versus the search for reward. Children do this seamlessly, but it is challenging computationally,&rdquo; notes Alison Gopnik, professor of psychology and affiliate professor of philosophy at the University of California at Berkeley, who was not involved with the project. &ldquo;This paper uses impressive new techniques to accomplish this automatically, designing an agent that can systematically balance curiosity about the world and the desire for reward, [thus taking] another step towards making AI agents (almost) as smart as children.&rdquo;&ldquo;Intrinsic rewards like curiosity are fundamental to guiding agents to discover useful diverse behaviors, but this shouldn&rsquo;t come at the cost of doing well at the given task. This is an important problem in AI, and the paper provides a way to balance that trade-off,&rdquo; adds Deepak Pathak, an assistant professor at Carnegie Mellon University, who was also not involved in the work. &ldquo;It would be interesting to see how such methods scale beyond games to real-world robotic agents.&rdquo;Chen, Hong, and Agrawal wrote the paper alongside Joni Pajarinen, assistant professor at Aalto University and research leader at the Intelligent Autonomous Systems Group at TU Darmstadt. The research was supported, in part, by the MIT-IBM Watson AI Lab, DARPA Machine Common Sense Program, the Army Research Office by the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator. The paper will be presented at Neural Information and Processing Systems (NeurIPS) 2022.<hr>&quot;Reprinted with permission of <a href="https://www.csail.mit.edu/news/ensuring-ai-works-right-dose-curiosity" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;&nbsp;

Curiosity drives artificial intelligence to explore the world, now in boundless use cases — autonomous navigation, robotic decision-making, optimizing health outcomes, and more.

Human-Computer Interaction

Latest Posts

Build2gether Inclusive Innovation Challenge: Winners Announced

Modbus TCP: A Comprehensive Guide to the Protocol and Its Applications

A new form of human-computer interaction

Podcast: More Curious AI To Save The World

High SNR microphones are transforming laptops into all-round communication hubs

Ensuring AI works with the right dose of curiosity