the novice is not going to be a very good partner to AI in learning. The novice doesn’t know enough to ask good questions. He will give the AI system a vague prompt about the goal of the exercise whereupon AI will sharpen it for the user. Essentially, it will chivvy the user toward the polished product, and the novice will simply accept the suggestions.

So the point of assignments is the mental processes required to complete them, and the point of the mental processes is learning. That seems to suggest a simple litmus test for the use of AI. Artificial Intelligence tools should not substitute for tasks wherein students would benefit from doing the mental work themselves.

Does AI stop children from learning?

Claude can even automatically learn from _other_ Slack channels and data sources, if it's granted permission.

Claude is learning how businesses actually operate: the context, the processes, the judgment.

Startups and Global 5000 companies alike are deploying Claude to handle complex workflows, and in doing so, Claude is learning how businesses actually operate: the context, the processes, the judgment.

Even when these tools are described as capable of “learning,” their way of doing so is different from that of a human person. It is not the experience of those who allow themselves to be shaped by life and grow over time through choices, mistakes, forgiveness and fidelity. Rather, it is a form of statistical adaptation based on data and feedback, which can be very effective, but does not imply inner growth.

Metacognitive Activities and Ethical Reflection

questioning the output, understanding limitations, and recognizing broader socioethical implications are essential for individuals to engage with such technologies in a constructive and responsible way

Metacognitive awareness

When juniors skip debugging and skip the formative mistakes, they don't build the tacit expertise. And when my generation of engineers retires, that knowledge doesn't transfer to the AI.

This is the first part I reject. The moving things around is precisely what thinking and writing involves. It's where ideas are born and cultivated, shaped to become what we have in mind. The rearranging of words to capture an incipient thought is the struggle and joy of being a writer.

Without a mechanism for continuous and diverse learning, AI systems will tend to reproduce the dominant patterns already present in their training data. That limitation would make truly creative work difficult.

The filing cabinet keeps getting bigger. But a bigger filing cabinet is still a filing cabinet. The breakthrough is letting the model do after deployment what made it powerful during training: compress, abstract, and learn.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

Uni-1 shows that learning to generate images materially improves fine-grained visual understanding performance, reasoning over regions, objects, and layouts.

Students Are Telling Us They Feel Invisible. We Should Listen.

AI for Efficiency - Using AI to Get Faster at Analysis Tasks

By "augmenting human intellect" we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems.

Matthew van der Hoorn Yes totally agree but could be used for creating a draft to work with, that's always the angle I try to take buy hear what you are saying Matthew!

***Deep Processing***-> It's important in learning. It's when our brain constructs meaning and says, "Ah, I get it, this makes sense." -> It's when new knowledge establishes connections to your pre-existing knowledge.-> When done well, It's what makes the knowledge easily retrievable when you need it. How do we achieve deep processing in learning? 👉🏽 STORIES, EXPLANATIONS, EXAMPLES, ANALOGIES and more - they all promote deep meaningful processing. 🤔BUT, it's not always easy to come up with stories and examples. It's also time-consuming. You can ask you AI buddies to help with that. We have it now, let's leverage it. Here's a microlesson developed on 7taps Microlearning about this topic.

Plenty of companies are still figuring out how to integrate “traditional AI” (that is, non-generative AI; tools like machine learning and rule-based algorithms)

Additionally, students in the Codex group were more eager and excited to continue learning about programming, and felt much less stressed and discouraged during the training.

On code-authoring tasks, students in the Codex group had a significantly higher correctness score (80%) than the Baseline (44%), and overall finished the tasks significantly faster. However, on the code-modifying tasks, both groups performed similarly in terms of correctness, with the Codex group performing slightly better (66%) than the Baseline (58%).

Can model-free reinforcement learning explain deontological moraljudgments?Alisabeth AyarsUniversity of Arizona, Dept. of Psychology, Tucson, AZ, USA

Searching as exploration. White and Roth [71 ,p.38] define exploratory search as a “sense making activity focusedon the gathering and use of information to foster intellectual de-velopment.” Users who conduct exploratory searches are generallyunfamiliar with the domain of their goals, and unsure about howto achieve them [ 71]. Many scholars have investigated the mainfactors relating to this type of dynamic task, such as uncertainty,creativity, innovation, knowledge discovery, serendipity, conver-gence of ideas, learning, and investigation [2, 46, 71].These factors are not always expressed or evident in queriesor questions posed by a searcher to a search system.

the canonical unit, the NCU supports natural capital accounting, currency source, calculating and accounting for ecosystem services, and influences how a variety of governance issues are resolved

“What are the enduring questions she should be asking herself?” Weiss said. “Is it OK to work alongside an AI for this type of task versus this type of task? Is it taking away from future opportunities or future skills she might have? I think students do have the capacity to reflect, but I’m not sure right now we’re giving them the right questions.”

alphago

Deep Learning (DL) A Technique for Implementing Machine LearningSubfield of ML that uses specialized techniques involving multi-layer (2+) artificial neural networksLayering allows cascaded learning and abstraction levels (e.g. line -> shape -> object -> scene)Computationally intensive enabled by clouds, GPUs, and specialized HW such as FPGAs, TPUs, etc.

Emergent abilities are not present in small models but can be observed in large models.

Technology like this, which lets you “talk” to people who’ve died, has been a mainstay of science fiction for decades. It’s an idea that’s been peddled by charlatans and spiritualists for centuries. But now it’s becoming a reality—and an increasingly accessible one, thanks to advances in AI and voice technology. 

because it only needs to engage a portion of the model to complete a task, as opposed to other architectures that have to activate an entire AI model to run every request.

many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs

Using chemicals to improve our economy of attention and become emotionally "fitter" is an option that penetrated public consciousness some time ago.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.

It doesn’t.What it does do is teach AI to recognize various things and fool you into thinking you’re getting better security.When you get something for free, you are the product.

Tech Literacy Resources

At the moment, GPT-2 uses a binary search algorithm, which means that its output can be considered a ‘true’ set of rules. If OpenAI is right, it could eventually generate a Turing complete program, a self-improving machine that can learn (and then improve) itself from the data it encounters. And that would make OpenAI a threat to IBM’s own goals of machine learning and AI, as it could essentially make better than even humans the best possible model that the future machines can use to improve their systems. However, there’s a catch: not just any new AI will do, but a specific type; one that uses deep learning to learn the rules, algorithms, and data necessary to run the machine to any given level of AI.

By comparison, Amazon’s Best Seller badges, which flag the most popular products based on sales and are updated hourly, are far more straightforward. For third-party sellers, “that’s a lot more powerful than this Choice badge, which is totally algorithmically calculated and sometimes it’s totally off,” says Bryant.

Algorithms will privilege some forms of ‘knowing’ over others, and the person writing that algorithm is going to get to decide what it means to know… not precisely, like in the former example, but through their values. If they value knowledge that is popular, then knowledge slowly drifts towards knowledge that is popular.

Some questions to use when discussing why we shouldn’t replace humans with AI (artificial intelligence) for learning

By utilizing the Deeplearning4j library1 for model representation, learning and prediction, KNIME builds upon a well performing open source solution with a thriving community.

It is especially thanks to the work of Yann LeCun and Yoshua Bengio (LeCun et al., 2015) that the application of deep neural networks has boomed in recent years. The technique, which utilizes neural networks with many layers and enhanced backpropagation algorithms for learning, was made possible through both new research and the ever increasing performance of computer chips.

Đầu tiên mình nghĩ bạn cần nắm về machine learning và algorithm, bạn có thể bắt đầu bằng các khóa học trên mạng. Mình recommend khóa học Machine Learning của Andrew Ng, khóa học này được coi là kinh thánh cho data scientist. Sau đó bạn có thể bắt đầu với Python hoặc R và tham gia challenge trên Kaggle. Kaggle là một platform để Data Scientist tham gia, kiếm tiền thưởng và cạnh tranh thứ hạng với nhau. Nhiều người cũng nói với mình Kaggle là con đường tốt nhất và ngắn nhất để đến với Data Science.

areas where deep learning is currently being poorly utilized

highly automated tools for training deep learning models

The best way we can help these people is by giving them the tools and knowledge to solve their own problems, using their own expertise and experience.

Big Data

OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.

Big Sur is our newest Open Rack-compatible hardware designed for AI computing at a large scale. In collaboration with partners, we've built Big Sur to incorporate eight high-performance GPUs