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Category: robotics/AI – Page 1,092

What is Prompt Engineering?

Artificial intelligence, particularly natural language processing, has a notion called prompt engineering (NLP). In prompt engineering, the job description is included explicitly in the input, such as a question, instead of being provided implicitly. Typically, prompt engineering involves transforming one or more tasks into a prompt-based dataset and “prompt-based learning”—also known as “prompt learning”—to train a language model. Prompt engineering, also known as “prefix-tuning” or “prompt tuning,” is a method wherein a big, “frozen” pretrained language model is used, and just the prompt’s representation is learned.

Developing the ChatGPT Tool, GPT-2, and GPT-3 language models was crucial for prompt engineering. Multitask prompt engineering in 2021 has shown strong performance on novel tasks utilizing several NLP datasets. Few-shot learning examples prompt with a thought chain provide a stronger representation of language model thinking. Prepaying text to a zero-shot learning prompt that supports a chain of reasoning, such as “Let’s think step by step,” may enhance a language model’s performance in multi-step reasoning tasks. The release of various open-source notebooks and community-led image synthesis efforts helped make these tools widely accessible.

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Common sense priors are essential to make decisions under uncertainty in real-world settings. Let’s say they want to give the scenario in Fig. 1 some labels. As a few key elements are recognized, it becomes evident that the image shows a restroom. This assists in resolving some of the labels for certain more difficult objects, such as the shower curtain in the scene rather than the window curtain and the mirror instead of the portrait on the wall. In addition to visual tasks, prior knowledge of expected item or event co-occurrences is crucial for navigating new environments and comprehending the actions of other agents. Moreover, such expectations are essential to object categorization and reading comprehension.

Unlike robot demos or segmented pictures, vast text corpora are easily accessible and include practically all aspects of the human experience. Current machine learning models use task-specific datasets to learn about the previous distribution of labels and judgments for the majority of problem domains. When training data is skewed or sparse, this can lead to systematic mistakes, particularly on uncommon or out-of-distribution inputs. How might they provide models with broader, more adaptable past knowledge? They suggest using learned distributions over natural language strings known as language models as task-general probabilistic priors.

LMs have been employed as sources of prior knowledge for tasks ranging from common-sense question answering to modeling scripts and tales to synthesizing probabilistic algorithms in language processing and other text production activities. They frequently give higher diversity and fidelity than small, task-specific datasets for encoding much of this information, such as the fact that plates are found in kitchens and dining rooms and that breaking eggs comes before whisking them. It has also been proposed that such language monitoring contributes to common-sense human knowledge in areas that are challenging to learn from first-hand experience.

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Early critiques pointed out that proving a network was near the critical point required improved statistical tests. The field responded constructively, and this type of objection is rarely heard these days. More recently, some work has shown that what was previously considered a signature of criticality might also be the result of random processes. Researchers are still investigating that possibility, but many of them have already proposed new criteria for distinguishing between the apparent criticality of random noise and the true criticality of collective interactions among neurons.

Meanwhile, over the past 20 years, research in this area has steadily become more visible. The breadth of methods being used to assess it has also grown. The biggest questions now focus on how operating near the critical point affects cognition, and how external inputs can drive a network to move around the critical point. Ideas about criticality have also begun to spread beyond neuroscience. Citing some of the original papers on criticality in living neural networks, engineers have shown that self-organized networks of atomic switches can be made to operate near the critical point so that they compute many functions optimally. The deep learning community has also begun to study whether operating near the critical point improves artificial neural networks.

The critical brain hypothesis may yet prove to be wrong, or incomplete, although current evidence does support it. Either way, the understanding it provides is generating an avalanche of questions and answers that tell us much more about the brain — and computing generally — than we knew before.

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Session kindly contributed by Silvester Sabathiel in SEMF’s 2021 Numerous Numerosity Workshop: https://semf.org.es/numerosity/

ABSTRACT
With the rise and advances in the field of artificial intelligence, opportunities to understand the finer-grained mechanisms involved in mathematical cognition have increased. A vast scope of related research has been conducted on machine learning systems that learn solving differential equations, algebraic equations and integrals or proofing complex theorems, all for which the preprocessed symbolic representations form the input and output types. However on the search for cognitive mechanisms that match the scope of humans when it comes to generalizability and applicability of mathematical concepts in the external world, a more grounded approach might be required. This involves starting with fundamental mathematical concepts that are earliest acquired in the human development and learning these within an interactive and multimodal environment. In this talk we are going to examine how artificial neural network systems within such a framework provide a controlled setup to discover possible cognitive mechanisms for intuitive numerosity perception or culturally acquired numerical concepts, such as counting. First we review impactful research results from the past, before I present the contributions of the work myself was involved in. Finally we can discuss the upcoming challenges for the field of numerical cognition and where this research journey could evolve to.

SILVESTER SABATHIEL
NTNU Trondheim.
Personal website: https://silsab.com/
NTNU profile: https://www.ntnu.edu/employees/silvester.sabathiel.
ResearchGate: https://www.researchgate.net/profile/Silvester-Sabathiel-3
LinkedIn: https://www.linkedin.com/in/silvester-sabathiel-03368b117

SEMF NETWORKS
Website: https://semf.org.es.
Twitter: https://twitter.com/semf_nexus.
LinkedIn: https://www.linkedin.com/company/semf-nexus.
Instagram: https://www.instagram.com/semf.nexus.
Facebook: https://www.facebook.com/semf.nexus

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Nice. But I’m more concerned about the jobs that will be lost from this.

#Zoox #youtube #ai #yahoofinance.
This segment originally aired on February 17, 2023.
Zoox CEO Aicha Evans joins Yahoo Finance Live anchors Dave Briggs and Seana Smith to discuss approval of its self-driving auto by the California DMV, the state of the autonomous vehicle industry, and Amazon’s ownership of the company.
Don’t Miss: Valley of Hype: The culture that built Elizabeth Holmes.
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Materials scientists are often inspired by nature and therefore use biological compounds as cues to design advanced materials. It is possible to mimic the molecular structure and functional motifs in artificial materials to offer a blueprint for a variety of functions. In a new report in Science Advances, Tae Hyun Kim and a research team at the California Institute of Technology and the Samsung Advanced Institute of Technology in the U.S. and South Korea, created a flexible biomimetic thermal sensing polymer, abbreviated BTS, which they designed to mimic ion transport dynamics of pectin; a plant cell wall component.

The researchers used a versatile synthetic procedure and engineered the properties of the to be elastic, flexible and stretchable in nature. The outperformed state-of-the-art temperature sensing materials such as vanadium oxide. Despite mechanical deformations, the thermal sensor-integrated material showed and stable functionality between 15° and 55° Celsius. The properties of the flexible BTS polymer made it well suited to map across space-time and facilitate broadband infrared photodetection relevant for a variety of applications.

Organic electronic materials are competitive alternatives to conventional silicon-based microelectronics due to their cost-effective, multifunctional nature. Materials scientists seek to tailor the properties of such materials at the molecular level for a range of sensing applications for wearable and implantable devices with specific characteristics such as flexibility and elasticity. At present, there is an increasing demand for all-organic electronic devices to form a range of soft and active materials. For instance, organic thermal sensors are suited for remote health care and robotics, albeit with limitations.

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https://youtube.com/watch?v=4LevUzfdBtw&feature=share

In this video, I’ll discuss some of the most advanced humanoid robots currently in development and reveal if the future really is bright for Robotics.

► All-New Echo Dot (5th Generation) | Smart Speaker with Clock and Alexa | Cloud Blue: https://amzn.to/3ISUX1u.
► Brilliant: Interactive Science And Math Learning: https://bit.ly/JasperAITechUniNet.

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* The Impact of Artificial Intelligence.
* Innovation In Space and New Scientific Discoveries.
* Entrepreneurial and Business Innovation.

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Continue reading “The Most Realistic Humanoid Robots In The World: How Will Artificial Intelligence Affect Our Future?” | >

Since OpenAI has not open-sourced the code for ChatGPT, replicating the chatbot is a herculean task, and even the big-tech are struggling. But, AI startup Colossal-AI has found a way to build your own ChatGPT with less computing resources.

Towards this goal, the company has leveraged a PyTorch-based implementation that covers all three stages from pre-training, reward model training, and reinforcement learning. They offer a demo version of the training process that requires only 1.62 GB of GPU memory and can be done on a single consumer-grade GPU, with 10.3x growth on one GPU model capacity.

Check out the GitHub repository here.

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