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Feb 05 2023

U.K. Central Bank and Treasury Believe Digital Pound is Needed

According to a story that was published by the Daily Telegraph on February 4, the Bank of England (BoE) and His Majesty’s Treasury feel that it is possible that the United Kingdom will need to develop a central bank digital currency (CBDC) by the year 2030.

According to a source inside the administration who spoke to the publication, the “digital pound” blueprint is going to be unveiled the following week. On February 7th, Deputy Governor Jon Cunliffe is going to provide an update on the work that the BoE has been doing on the CBDC.

The Governor of the Bank of England, Andrew Bailey, and the Chancellor of the Exchequer, Jeremy Hunt, were quoted in the Telegraph as saying that they believe it is likely that a digital version of the pound will be required in the future. This conclusion was reached on the basis of the work that has been done up until this point.

The Bank of England did not comment on the report but did say that a joint consultation about the digital pound will be made available in the near future.

It was reported that cash and coin payments in the United Kingdom dropped by 35% in the year 2020. About one payment out of every six is made with cash, while the other five are made using debit and credit cards. A digital currency produced by a central bank is a digital representation of government-issued money that is pegged to fiat reserves on a one-to-one basis.

The announcement was made only a few days after HM Treasury published a job listing on LinkedIn advertising an open position for a head of central bank digital currency. The function was described as “important, complicated, and cross-cutting” in the job description, and it was said that it required “substantial involvement within and beyond the HM Treasury.”

The digital pound is only one of the numerous CBDCs that are anticipated to be implemented in different parts of the globe in the years to come. The European Central Bank has been debating the possibility of a digital version of the euro, and a number of other nations, notably Sweden and Denmark, are also looking into the possibility of adopting digital currencies.

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Feb 02 2023

Senate Banking Committee to Develop Bipartisan Regulatory Framework for Cryptocurrencies

Recent reports indicate that Republican United States Senator Tim Scott, who serves as the ranking member of the Senate Banking Committee, aims to build “a bipartisan regulatory framework” for virtual currencies. Senator Scott is the ranking member of the Senate Banking Committee.

In a piece that was published on the 2nd of February by Politico, it was said that Scott has prioritised the creation of a cryptographic framework as one of his primary objectives for the 118th Congress. Reportedly, he had some reservations about certain aspects of cryptocurrency, citing the failure of exchanges such as FTX — “high-profile failures resulting in lost client money” — as well as the possibility that it may be used for illegal financing. He also cited the possibility that it may be used for illegal financing.

Recent events have resulted in Scott being promoted to the position of ranking member, which was originally held by the late Senator Pat Toomey. Toomey completed the balance of his tenure in office but did not seek reelection in the following year. Toomey lent his support to a number of legislative initiatives aimed at fostering innovation in the digital asset sector, and Sherrod Brown, the committee chair, urged Treasury Secretary Janet Yellen to collaborate with financial regulators and legislators on comprehensive cryptocurrency legislation. Toomey lent his support to a number of legislative initiatives aimed at fostering innovation in the digital asset sector.

The Senate Banking Committee held a hearing in December with the purpose of reviewing the failure of FTX, which had occurred earlier in the month. When a new session of Congress starts in 2023, there is a possibility that the Committee may continue its investigation as part of that session. The House Financial Services Committee, which is now led by Representative Patrick McHenry and has the ability to hold another hearing on FTX, may also hold such a hearing at some point in the near future.

As a direct consequence of the Republican Party’s win in the House of Representatives, McHenry now has the authority to choose which topics need to be prioritised on the legislative agenda for the finance committee. It is believed that he aims to form a new subcommittee that would be focused on digital problems. This would be in response to the “vast hole” that reportedly existed in the existing committee structures.

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Jan 31 2023

SEC Loses Appeal in LBRY Vs. SEC

The United States Securities and Exchange Commission (SEC) has acknowledged publicly in the public record that the secondary market sale of LBRY Credits (LBC) tokens does not constitute the sale of a security. On January 30, an appeal hearing was held in the case LBRY vs. SEC. The settlement was reached at that hearing.

During the appeal hearing, Attorney John Deaton put an end to a key discussion, which many people hailed as a triumph for the whole cryptocurrency sector in its fight against the SEC’s overreach regulation by enforcement.

During the hearing that took place on November 7, 2022, the SEC was granted a summary judgement in its favour. The court’s decision characterised every sale of LBC tokens that took place over a period of six years as an investment contract, but it did not go into more detail about the particulars of each transaction. The Securities and Exchange Commission (SEC) had high hopes that it could progress its campaign to obtain credibility in the secondary market and bring it within its supervision at the same time. The Securities and Exchange Commission has requested that the judge of the district court in New Hampshire uphold the broad and vague injunction that prohibits the company’s sale.

Because he believed the injunction to be too wide and imprecise, Deaton, who was acting in the capacity of amicus curiae for tech journalist Naomi Brockwell, pushed for clarification about secondary market transactions for LBC. An amicus curiae is a person or organisation that is not a party to a legal case but is authorised to assist a court by providing information, expertise, or insight that is relevant to the issues that are being litigated in the case. This can be done in order to shed light on the circumstances surrounding the case.

A study written by commercial contract attorney Lewis Cohen that studied all security litigation filed in the United States since the SEC vs. W.J. Howey Co case was mentioned by Deaton. During the course of his investigation into security matters in the United States, Cohen came across not a single instance in which a judge admitted that the underlying asset was security.

Deaton was successful in arguing to the court that LBC’s transactions on the secondary market did not include securities. In an attempt to circumvent the need to provide clarity for LBC, the SEC submitted a request for an order that draws no distinctions between LBRY, the management of the firm, and its users. The court then addressed Amici as follows: “amicus, I’m going to make it plain that my order does not extend to secondary market sales.” The judge then turned to face Deaton.

The decision in the lawsuit provided a sense of comfort for many members of the cryptocurrency community, particularly owners of XRP. Ripple is now being sued by the Securities and Exchange Commission (SEC) on the selling of XRP tokens. The new judgement that shows the selling of LBC tokens on secondary markets does not constitute as securities may work in favour of Ripple in the protracted case that they are currently engaged in. According to a Twitter account that supports XRP, the judgement also classifies XRP as a non-security.

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SEC Loses Appeal in LBRY Vs. SEC Republished from Source https://blockchain.news/news/sec-loses-appeal-in-lbry-vs-sec via https://blockchain.news/RSS/

Jan 30 2023

Could you really replace Jeremy Hunt with ChatGPT?

Jeremy Hunt’s use of ChatGPT to help write the introduction to his pro-innovation speech on Friday felt both neat and unfortunate.

Neat to highlight the uses of such advanced software; unfortunate to raise the thought that much of the government’s policy on tech can too easily be drawn up by an AI chatbot.

In fact, asking ChatGPT for a ten-point plan to boost innovative growth yields some promising but familiar ideas: investment in R&D, support for start-ups and access to funding.

It is the sort of ambitious but proposal-light stuff that we’ve heard from ministers hoping to make the UK “the next Silicon Valley.” But it is no longer cutting it for many in the sector at a time when the UK is in a post-Brexit race to attract and keep innovative companies.

Tech founders and investors are already talking about how they can use the $369bn on offer under the US Inflation Reduction Act for technology including electric vehicles, hydrogen and carbon capture.

A response by EU states would leave the UK squeezed between two rival regions fighting over the most exciting future technologies. In Europe, countries such as France have been quick to roll out the red carpet for tech firms to relocate and grow.

Yet UK government policy has recently been slightly puzzling. Take the decision to cut back the R&D tax credit scheme for start-ups that many have relied on to grow in recent years. In a survey by industry body Coadec of more than 250 UK founders, almost everyone said the cuts made the UK significantly less attractive.

Of course, tax credits can and should be reformed to address fraud risks. But such incentives are one of the most important levers the UK has to help lossmaking start-ups in their early days. If anything, they should be expanded. Likewise, renewing or extending tax reliefs offered by the Enterprise Investment Scheme and Seed Enterprise Investment Scheme in the long term should be a priority.

The broader environment also matters if those tax incentives are to lead to businesses that stick around. Founders need a functioning exit for their firms in the shape of a London stock market equipped to provide both analyst coverage and investor interest. Promises to reform the listed market, including fixing the dulling effect on analyst research from Mifid II, need to happen quickly to make London more attractive to tech firms.

The government can help address funding gaps for start-ups and scale-ups in the UK. Shovelling the same money around won’t help: a forthcoming semiconductor review, for example, needs to be clear about new financial support for the sector, rather than simply pointing to existing funds marshalled by the British Business Bank and Innovate UK.

The government is giving much weight to the outcome of a regulatory review by Patrick Vallance in some of these areas. But this will be little use to the sector if the answer is more strategies, reviews and task forces with little to back them up.

Tinkering with rules in search of short term Brexit “benefits” may not mean long-term structural improvements. Take the £100bn Brexit win ministers highlight from changes to Solvency II insurance regulation: there is no guarantee that British pension funds will invest in British assets, let alone innovative sectors, given their duty to find attractive returns regardless of nationality or sector.

The UK government needs to create the backdrop to ensure there are attractive firms and industries in which to invest, in both private and public markets.

And deregulation can’t solve everything: the top Brexit-related request from founders is that the visa system for talented workers be expanded and streamlined so they can get the staff they need.

The chancellor is right to call out the successes of the tech sector: London in particular is a tech leader in Europe, raising more funds for more businesses than rival cities in recent years.

But regulatory reforms need to be matched with broader policy and financial and fiscal support for industries that should repay many times over in taxes and employment. Otherwise ambitions to become the next Silicon Valley will remain just that.

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Could you really replace Jeremy Hunt with ChatGPT? Republished from Source https://www.ft.com/content/972a5b39-80c4-4613-8aff-dc34c2a506a3 via https://www.ft.com/companies/technology?format=rss

Jan 30 2023

Talking to Robots in Real Time

Posted by Corey Lynch, Research Scientist, and Ayzaan Wahid, Research Engineer, Robotics at Google

A grand vision in robot learning, going back to the SHRDLU experiments in the late 1960s, is that of helpful robots that inhabit human spaces and follow a wide variety of natural language commands. Over the last few years, there have been significant advances in the application of machine learning (ML) for instruction following, both in simulation and in real world systems. Recent Palm-SayCan work has produced robots that leverage language models to plan long-horizon behaviors and reason about abstract goals. Code as Policies has shown that code-generating language models combined with pre-trained perception systems can produce language conditioned policies for zero shot robot manipulation. Despite this progress, an important missing property of current “language in, actions out” robot learning systems is real time interaction with humans.

Ideally, robots of the future would react in real time to any relevant task a user could describe in natural language. Particularly in open human environments, it may be important for end users to customize robot behavior as it is happening, offering quick corrections (“stop, move your arm up a bit”) or specifying constraints (“nudge that slowly to the right”). Furthermore, real-time language could make it easier for people and robots to collaborate on complex, long-horizon tasks, with people iteratively and interactively guiding robot manipulation with occasional language feedback.

The challenges of open-vocabulary language following. To be successfully guided through a long horizon task like “put all the blocks in a vertical line”, a robot must respond precisely to a wide variety of commands, including small corrective behaviors like “nudge the red circle right a bit”.

However, getting robots to follow open vocabulary language poses a significant challenge from a ML perspective. This is a setting with an inherently large number of tasks, including many small corrective behaviors. Existing multitask learning setups make use of curated imitation learning datasets or complex reinforcement learning (RL) reward functions to drive the learning of each task, and this significant per-task effort is difficult to scale beyond a small predefined set. Thus, a critical open question in the open vocabulary setting is: how can we scale the collection of robot data to include not dozens, but hundreds of thousands of behaviors in an environment, and how can we connect all these behaviors to the natural language an end user might actually provide?

In Interactive Language, we present a large scale imitation learning framework for producing real-time, open vocabulary language-conditionable robots. After training with our approach, we find that an individual policy is capable of addressing over 87,000 unique instructions (an order of magnitude larger than prior works), with an estimated average success rate of 93.5%. We are also excited to announce the release of Language-Table, the largest available language-annotated robot dataset, which we hope will drive further research focused on real-time language-controllable robots.

Guiding robots with real time language.

Real Time Language-Controllable Robots

Key to our approach is a scalable recipe for creating large, diverse language-conditioned robot demonstration datasets. Unlike prior setups that define all the skills up front and then collect curated demonstrations for each skill, we continuously collect data across multiple robots without scene resets or any low-level skill segmentation. All data, including failure data (e.g., knocking blocks off a table), goes through a hindsight language relabeling process to be paired with text. Here, annotators watch long robot videos to identify as many behaviors as possible, marking when each began and ended, and use freeform natural language to describe each segment. Importantly, in contrast to prior instruction following setups, all skills used for training emerge bottom up from the data itself rather than being determined upfront by researchers.

Our learning approach and architecture are intentionally straightforward. Our robot policy is a cross-attention transformer, mapping 5hz video and text to 5hz robot actions, using a standard supervised learning behavioral cloning objective with no auxiliary losses. At test time, new spoken commands can be sent to the policy (via speech-to-text) at any time up to 5hz.

Interactive Language: an imitation learning system for producing real time language-controllable robots.

Open Source Release: Language-Table Dataset and Benchmark

This annotation process allowed us to collect the Language-Table dataset, which contains over 440k real and 180k simulated demonstrations of the robot performing a language command, along with the sequence of actions the robot took during the demonstration. This is the largest language-conditioned robot demonstration dataset of its kind, by an order of magnitude. Language-Table comes with a simulated imitation learning benchmark that we use to perform model selection, which can be used to evaluate new instruction following architectures or approaches.

Dataset	# Trajectories (k)	# Unique (k)	Physical Actions	Real	Available
Episodic Demonstrations
BC-Z	25	0.1	✓	✓	✓
SayCan	68	0.5	✓	✓	❌
Playhouse	1,097	779	❌	❌	❌
Hindsight Language Labeling
BLOCKS	30	n/a	❌	❌	✓
LangLFP	10	n/a	✓	❌	❌
LOREL	6	1.7	✓	✓	✓
CALVIN	20	0.4	✓	❌	✓
Language-Table (real + sim)	623 (442+181)	206 (127+79)	✓	✓	✓

We compare Language-Table to existing robot datasets, highlighting proportions of simulated (red) or real (blue) robot data, the number of trajectories collected, and the number of unique language describable tasks.

Learned Real Time Language Behaviors

Examples of short horizon instructions the robot is capable of following, sampled randomly from the full set of over 87,000.

Short-Horizon Instruction	Success
(87,000 more…)	…
push the blue triangle to the top left corner	80.0%
separate the red star and red circle	100.0%
nudge the yellow heart a bit right	80.0%
place the red star above the blue cube	90.0%
point your arm at the blue triangle	100.0%
push the group of blocks left a bit	100.0%
Average over 87k, CI 95%	93.5% +- 3.42%

95% Confidence interval (CI) on the average success of an individual Interactive Language policy over 87,000 unique natural language instructions.

We find that interesting new capabilities arise when robots are able to follow real time language. We show that users can walk robots through complex long-horizon sequences using only natural language to solve for goals that require multiple minutes of precise, coordinated control (e.g., “make a smiley face out of the blocks with green eyes” or “place all the blocks in a vertical line”). Because the robot is trained to follow open vocabulary language, we see it can react to a diverse set of verbal corrections (e.g., “nudge the red star slightly right”) that might otherwise be difficult to enumerate up front.

Examples of long horizon goals reached under real time human language guidance.

Finally, we see that real time language allows for new modes of robot data collection. For example, a single human operator can control four robots simultaneously using only spoken language. This has the potential to scale up the collection of robot data in the future without requiring undivided human attention for each robot.

One operator controlling multiple robots at once with spoken language.

Conclusion

While currently limited to a tabletop with a fixed set of objects, Interactive Language shows initial evidence that large scale imitation learning can indeed produce real time interactable robots that follow freeform end user commands. We open source Language-Table, the largest language conditioned real-world robot demonstration dataset of its kind and an associated simulated benchmark, to spur progress in real time language control of physical robots. We believe the utility of this dataset may not only be limited to robot control, but may provide an interesting starting point for studying language- and action-conditioned video prediction, robot video-conditioned language modeling, or a host of other interesting active questions in the broader ML context. See our paper and GitHub page to learn more.

Acknowledgements

We would like to thank everyone who supported this research. This includes robot teleoperators: Alex Luong, Armando Reyes, Elio Prado, Eric Tran, Gavin Gonzalez, Jodexty Therlonge, Joel Magpantay, Rochelle Dela Cruz, Samuel Wan, Sarah Nguyen, Scott Lehrer, Norine Rosales, Tran Pham, Kyle Gajadhar, Reece Mungal, and Nikauleene Andrews; robot hardware support and teleoperation coordination: Sean Snyder, Spencer Goodrich, Cameron Burns, Jorge Aldaco, Jonathan Vela; data operations and infrastructure: Muqthar Mohammad, Mitta Kumar, Arnab Bose, Wayne Gramlich; and the many who helped provide language labeling of the datasets. We would also like to thank Pierre Sermanet, Debidatta Dwibedi, Michael Ryoo, Brian Ichter and Vincent Vanhoucke for their invaluable advice and support.

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Talking to Robots in Real Time Republished from Source http://ai.googleblog.com/2022/12/talking-to-robots-in-real-time.html via http://feeds.feedburner.com/blogspot/gJZg

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