Sign into an existing account

https://www.forbes.com/sites/arunshastri/2021/03/22/2021-ai-index-four-ai-trends-that-stood-out/

1. The center of gravity appears to be shifting to China, but that could be a mirage. Years ago, the story was that the US was falling behind in math and science. Today, we’re hearing similar stories about China’s successes in AI. The report shows that China has overtaken the U.S. in the number of AI journal citations, and other such warnings abound. However, the report also shows that the U.S. is attracting Ph.D. students and they’re staying in the U.S. after they complete their studies. It also shows that 65% of graduating Ph.D. students in North America move into industry careers. These two trends suggest that while China may be ahead in AI research, the U.S. will continue to pull AI talent from around the world (including China) and graduate them into the private sector, which will drive increased hiring and attract more talent. China may be taking the lead, but I’m bullish on the U.S.’s ability to win the marathon.

2. New ways of addressing old problems will have far reaching consequences. The report highlighted drug discovery, content generation and surveillance as growth opportunities for AI. These are historically human domains that are increasingly being driven by artificial intelligence. The impact that these investments will have on our way of life, for good or ill, can’t be underestimated. Take drug development. A pharma company’s return on investment for research and development is at an all-time low (less than 2%). The promise of AI is that it can be used to improve this ROI by quickening the pace of discovery, and patients around the world will feel the impact of new therapies.

3. Despite all the talk of ethics, we’re not seeing enough action. Two issues in the report highlighted areas where AI is struggling: Its lack of diversity and ethical consensus and benchmarks. We all know that diversity is sorely needed. Racial and myriad other identities need representation in AI. To build these teams, we must address both diversity and equity. Not only is it the right thing to do, but diverse teams also make better AI. Bias is not the only ethical concern that we’re waking up to. AI’s societal implications are profound and fortunately many are asking what can and can’t be done to protect humankind as we drive these advancements. We can’t wring our hands and do nothing. We also can’t woke wash and window dress our way through technological evolution. We must find consensus in the AI community, set benchmarks and do the work that needs to be done. This brings me to my final observation:

4. Government regulation is coming, but we must not wait for it. As the report indicates, Congress has finally taken notice of AI. But it’s still Congress. Regulation may be years away and may arrive watered down or too restrictive. It’s up to the private sector to take steps to self-regulate immediately. They have the resources and the incentive. Sales will only suffer from the disruption that can be caused by unethical technology, and if they self-regulate adequately, the private sector may be able to avoid over-regulation.

https://syncedreview.com/2021/03/22/deepmind-proposes-manipulation-independent-representations-for-imitation-of-behaviours-demonstrated-by-previously-unseen-manipulator-morphologies/

The basic idea behind imitation learning is implicitly giving a robotic entity prior information about the world by mimicking human behaviour. Through such techniques, which can be likened to behaviour cloning or inverse reinforcement learning, it is possible to increase the efficiency of robots and their interaction capacities with humans, while also decreasing the cost involved in learning new skills.

Inspired by the idea that a robotic manipulator can imitate any visually-demonstrated behaviour of arbitrary complexity, a DeepMind research team has proposed manipulation-independent representations (MIR) that can support successful imitation of behaviours demonstrated by previously unseen manipulator morphologies using only visual observations.

image.png
The researchers first explain how to imitate unconstrained manipulation trajectories executed by previously unknown manipulators, specifically how to learn pixel-based representations that produce the right information for a cross-embodiment trajectory tracking agent. The proposed imitation method has two main phases: learning an MIR space, and cross-embodiment visual imitation through reinforcement learning using the pretrained MIR space.

In the evaluations, MIR achieved the best performance across all test domains, significantly boosting Jaco Hand performance on stacking success and perfectly imitating the simulated Jaco Hand and Invisible Arm on lifting, with a 100 percent score.

Overall, the study revealed the important considerations in learning manipulator and task-independent representations for imitation, and validated the proposed MIR’s characteristics for successful cross embodiment visual imitation.

The paper Manipulator-Independent Representations for Visual Imitation is on arXiv.

https://arxiv.org/pdf/2103.09016.pdf

https://futurism.com/the-byte/sam-altman-warns-ai-destroy-jobs

This kike loves talking out of both sides of his mouth

https://greatgameindia.com/which-countries-are-most-interested-in-generative-ai/

Data provided by ElectronicsHub has revealed which countries are most interested in generative AI:

Text-: Philippines, Singapore, Canada
Image-: Israel, Singapore, Finland
Audio-: Uruguay, Chile, Argentina
Video-: Singapore, UAE, Cyprus

Generative AI blurs the boundaries between fake and true, as well as between real and unreal...

are only white people encouraged to have a raceless identity by anti-racism?


Copilot


Anti-racism is a multifaceted movement that aims to dismantle systemic racism and promote equality. While it's true that discussions around racelessness and race identity are relevant to everyone, the concept of a raceless identity is not exclusive to any particular racial or ethnic group. Let's explore this further:
- The Theory of Racelessness:
- The Theory of Racelessness advocates for the elimination of racism and the corresponding belief in race. It challenges the idea that "race" is an inherent and unchangeable aspect of human identity.
- According to this theory, ending racism requires us to dismantle the very concept of "race" itself. By doing so, we can better identify and analyze racism without inadvertently reinforcing it through language and ideology .
- Racelessness is not about denying cultural heritage or ignoring racial disparities. Instead, it encourages a shift away from racial categorization and toward a more unified, inclusive understanding of humanity.
- Colorblind Ideology:
- Colorblind ideology is another perspective often associated with anti-racism. It aims to treat individuals equally regardless of their race or ethnicity.
- However, critics argue that colorblindness can inadvertently dismiss the unique experiences of people of color, invalidate their perspectives, and overlook historical and systemic inequalities.
- While colorblindness seeks to promote equality, it may unintentionally perpetuate existing racial disparities.
- Raceless Identity Beyond White People:
- Racelessness is not limited to any specific racial group. People of all backgrounds can explore a raceless identity.
- For instance, the book "Raceless: In Search of Family, Identity, and the Truth about Where I Belong" by Georgina Lawton discusses her personal journey of grappling with identity and belonging, even though she was raised in a predominantly white environment.
- The goal is to move beyond rigid racial categories and recognize our shared humanity.
In summary, the pursuit of a raceless identity is relevant to everyone, regardless of their racial background. It challenges us to rethink how we define ourselves and others, ultimately contributing to a more equitable and inclusive society.

With Whites as a raceless group, it means they can't unify and fight against the White Genocide or world Jewry.