Today is Esperanto Day – here’s why I learned it
- That’s how you make a verb (a doing-word) refer to the present tense in Esperanto.
- You probably don’t believe me, but it’s true – if having the sort of grammar that brings tears of joy to the eyes of its learners was a crime, Esperanto would be doing life in maximum security.
- Adverbs, adjectives, participles, and every other grammatical aspect of Esperanto has a similar sense of structure and consistency like the examples above, making it much easier to learn.
- Now you know a little more about Esperanto, and why this particular day of the year is known as Esperanto Day. Imagine your interest was asked to describe itself in one word.
- If you do begin learning or playing with Esperanto, I wish you bonŝancon (good luck), and please feel free to reach out if you’d like any more pointers or have any questions.
Here’s how you can master the latest AI technologies
- The Deep Learning and Artificial Intelligence Introductory Bundle breaks down what can understandably be an intimidating AI education into simple, digestible parts that appeal even to those who lack a more rigorous background in the field, and the entire bundle is currently available for over 90 percent off at just $39.
- From there, you’ll dive into more advanced elements of the field with instruction that teaches you about using Python for logistic regression, how to build powerful predictive models that can forecast data outputs, how to work with some of the most popular Deep Learning techniques using Theano and TensorFlow, and more.
- Sponsored posts are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked.
How meditation impacts the way we learn
- In a new study, researchers from the University of Surrey in the United Kingdom focused on one particular type of meditation — "focused attention meditation" — and whether it affects how a person learns.
- For the purpose of this study, the investigators trained the participants to do well in an activity in which they had to select images that were most likely to bring them a particular reward.
- Opitz and Knytl explain, suggests that meditators tend to learn from positive outcomes, while non-meditators most likely learn from negative outcomes.
- The scientists also note that previous research has found that people with Parkinson's disease — who have much lower levels of dopamine than normal — tended not to perform well on learning tasks that required them to respond to positive feedback.
Baby gene edits could affect a range of traits
- He, a genome-editing researcher at the Southern University of Science and Technology of China in Shenzhen, says in several YouTube videos that he impregnated a woman with embryos that had been edited to disable a gene that allows HIV to infect cells.
- He targeted this gene, known as CCR5, because it is well studied, and because its mutation offers protection against HIV infection, which still carries a significant social stigma in China.
- Although the CCR5-Δ32 mutation disables the gene and makes carriers resistant to the dominant strain of HIV, over the past two decades dozens of studies have shown that CCR5 also helps to protect the lungs, liver and brain during some other serious infections and chronic diseases.
- Murphy says that the twin with one copy of the gene should be protected from these severe effects if she contracts the virus, but the other twin probably has a higher risk of complications if infected.
Machine learning is gradually changing modern agricultural practices
- In the recent years, plant breeders have been looking for specific trait that suits environmental conditions by looking for traits that will help a certain type of a crop that uses water efficiently together with nutrients, climate change, and resist disease.
- Machine learning uses this information that is beyond human grasp to predict which particular genes are most likely to contribute into a beneficial trait.
- These computer simulations are used by scientists to conduct early tests to evaluate productivity of a variety of crops and their performance under different climatic conditions, weather patterns, soil types, and other factors.
- Machine learning in the agricultural field allows more accurate disease diagnosis – eliminating any inefficiencies and time wastage.
- In a nutshell – machine learning is like farming; nutrient is the main data, gardener is the farmer, seed is the algorithm while the plant is the program.
How AI Training Scales
- We've discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks.
- Specifically, we did training runs at a wide range of batch sizes (tuning the learning rate separately for each) for all of these tasks and compared the speedups in training to what the noise scale predicts should happen.
- Since large batch sizes often require careful and expensive tuning or special learning rate schedules to be effective, knowing an upper limit ahead of time provides a significant practical advantage in training new models.
- In particular, we have evidence that more difficult tasks and more powerful models on the same task will allow for more radical data-parallelism than we have seen to date, providing a key driver for the continued fast exponential growth in training compute.
Machine Learning Trick of the Day (8): Instrumental Thinking
- If this assumption is actually true for the problem we are addressing—that features x are linearly related to targets y using a set of parameters \beta, and noise only affects the targets—then we can also call our model a structural model (or structural equation).
- Consider what is known as an errors-in-variables scenario1 (see figure 1 (centre)): a regression problem where the same source of noise \epsilon affects the features and the target.
- The instrumental variables trick asks us to use the data itself to account for noise, and makes it easier for us to define structural models and to make causal predictions.
- But we do have a trick for such scenarios: we can use instrumental variables regression and remain able to learn value-function parameters that correctly capture the causal structure of future rewards.
- Like every trick in this series, the instrumental variables give us an alternative way to think about existing problems.
AI Transformation Playbook – How to lead your company into the AI era
- Once other teams started to see the success of Google Speech working with Google Brain, we were able to acquire more internal customers.
- Build several difficult AI assets that are broadly aligned with a coherent strategy: AI is enabling companies to build unique competitive advantages in new ways.
- Leverage AI to create an advantage specific to your industry sector: Rather than trying to compete “generally” in AI with leading tech companies such as Google, I recommend instead becoming a leading AI company in your industry sector, where developing unique AI capabilities will allow you to gain a competitive advantage.
- Expecting an AI team to magically create value from a large dataset is a formula that comes with a high chance of failure, and I have tragically seen CEOs over-invest in collecting low-value data, or even acquire a company for its data only to realize the target company’s many terabytes of data is not useful.
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- If we can reduce friction to finding higher impact work, we’ll help people be more productive, feel more fulfilled, and ultimately accelerate human progress.
An Open Source Tool for Scaling Multi-Agent Reinforcement Learning
- In this blog post we introduce general purpose support for multi-agent RL in RLlib, including compatibility with most of RLlib’s distributed algorithms: A2C / A3C, PPO, IMPALA, DQN, DDPG, and Ape-X.
- In the remainder of this blog post we discuss the challenges of multi-agent RL, show how to train multi-agent policies with these existing algorithms, and also how to implement specialized algorithms that can deal with the non-stationarity and increased variance of multi-agent environments.
- This can be done in a way by swapping weights between two different trainers (there is a code example here), but this approach won’t scale with even more types of algorithms thrown in, or if e.g., you want to use experiences to train a model of the environment at the same time.