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OpenAI is seeking the support of its most significant benefactor

Technical advancements are becoming increasingly vital in determining the course of B2B payments. Supporting businesses with advanced delivery models incorporating a variety of payment methods, including card-not-present transactions, electronic invoices, and omnichannel experiences, in addition to addressing the perennial B2B frictions inherent in cross-border payments, are critical areas of innovation within AP and AR processes.

However, he noted that in B2B, security and certainty of payments are becoming more important than payment speed. As a result, real-time payments and ACH are becoming more appealing than paper checks. And despite the continued prevalence of net terms in payments for small to medium-sized businesses (SMBs) and mid-market business-to-business (B2B), innovation is producing alternatives such as dynamic payment terms and pricing models.

In an interview, Sam Altman, the chief executive officer of the artificial intelligence (AI) firm, revealed his intentions to obtain further financial support from Microsoft. Microsoft has already committed $10 billion to finance AGI, software designed to emulate human intelligence. Altman stated that his companys collaboration with Microsoft and its CEO Satya Nadella was extremely fruitful and that he anticipated raising a substantial amount more over time from Microsoft and other investors to cover the expenses associated with developing more complex AI models. When asked whether Microsoft would persist, Altman responded, I certainly hope so. There is still much computing to develop between now and AGI, he continued. Training costs are simply enormous. Following last weeks Developers Day, where OpenAI unveiled a marketplace showcasing its finest applications and a suite of new tools and enhancements to its GPT-4, as well as a revenue-sharing model with the most popular GPT creators, he made these remarks.

In the interim, PYMNTS has recently examined the obstacles the government faces in its efforts to regulate AI. Comprehension of the technologys operation and acquisition of the requisite expertise to supervise it are among the most urgent matters.

In contrast to historical AI implementations such as machine learning and predictive forecasting, which have become ubiquitous in various aspects of daily life, generative AI capabilities introduce a novel approach to automating and producing outputs in domains such as investment research, risk management, trading, and fraud detection.

Read the Latest blog from us: AI And Cloud- The Perfect Match

Additionally, recognizing the intricacy of ostensibly straightforward matters can yield advantageous outcomes in the long run. Furthermore, it is worth noting that the priorities of organizations operating in the B2B payments sector are influenced by macroeconomic factors, especially considering the current prolonged economic expansion. A growing number of developments in the payments industry are conforming to these priorities above.

In addition, organizations are progressively seeking vendor consolidation as a means to mitigate overall risk by restricting the number of technology vendors that interact with their ecosystem, according to Weiner. Furthermore, he noted that CTOs and CFOs are collaborating more frequently on B2B transformations. The advent of digital payments has resulted in enhanced transparency and instantaneous understanding of financial activities. Weiner, on the other hand, believes that while real-time payments offer efficiency and security benefits, they may not be a game-changer in B2B payments, where the majority of transactions are conducted on net terms.

Read:AI and Machine Learning Are Changing Business Forever

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OpenAI Is Seeking Additional Investment in Artificial General ... - AiThority

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Richard Sutton, a computer scientist and well known AI researcher, at home in Edmonton, Alta., on Nov. 23. Prof. Sutton is launching a new AI research institute in Edmonton with funding from Huawei.Amber Bracken/The Globe and Mail

One of the countrys most accomplished artificial intelligence researchers is launching a new non-profit lab with $4.8-million in funding from Huawei Canada, after the federal government restricted the Chinese companys ability to work with publicly funded universities.

Richard Sutton, a professor at the University of Alberta and a pioneer in the field of reinforcement learning, says the Openmind Research Institute will fund researchers following the Alberta Plan, a 12-step guide he co-authored last year that lays out a framework for pursuing the development of AI agents capable of human-level intelligence.

Openmind will be based in Edmonton and kicks off Friday with a weekend retreat in Banff.

Canada banned the use of equipment from Huawei in 5G networks last year, citing the company as a security risk because of its connections to the Chinese government, which could use the company for espionage. Huawei has long denied the accusation.

Jim Hinton, a Waterloo, Ont.-based patent lawyer and senior fellow at the Centre for International Governance Innovation, said Huaweis involvement with Openmind raises concerns. Even if the money is coming with as little strings attached as possible, there is still soft power that is being wielded, he said. The fact that theyre holding the purse strings gives a significant amount of control.

In 2021, Ottawa started restricting funding for research collaborations between publicly funded universities and entities with links to countries considered national security risks, including China. Alberta has implemented similar restrictions for sensitive research at a provincial level. Artificial intelligence is particularly sensitive because the technology has military applications and can be used for nefarious purposes.

I hope that it could counter that narrative and be an example of how things could be really good, Prof. Sutton said of Openmind and Huaweis funding. This is a case where the interaction with China has been really productive, really valuable in contributing to open AI research in Canada.

All of the work done by Openmind, which is separate from Prof. Suttons role at the University of Alberta, will be open-source, and the institute will not pursue intellectual property rights.

Nor will Huawei. I was a little bit surprised that they were willing to do something so open and with no attempt at control, said Prof. Sutton, who has a long-standing relationship with Huawei in Alberta.

Huawei did not respond to requests for comment.

Although the Chinese company has been shut out of 5G networks and restricted in working with universities in Canada, it can still work directly with individual researchers.

Companies linked to Chinas military, like Huawei is, will try to find other ways around the federal rules, including directly funding researchers outside university institutions. It appears Huawei is doing exactly that, said Margaret McCuaig-Johnston, a senior fellow at the Institute for Science, Society and Policy at the University of Ottawa. China pushes the envelope as far as they can.

Prof. Sutton wrote the textbook literally on reinforcement learning, which is an approach to developing AI agents capable of performing actions in an environment to achieve a goal. Reinforcement learning is everywhere in the world of AI, including in autonomous vehicles and in how chatbots such as ChatGPT are polished to sound more human.

Born in the United States, Prof. Sutton completed a PhD at the University of Massachusetts in 1984 and worked in industry before returning to academia. He joined the University of Alberta in 2003, where he founded the Reinforcement Learning and Artificial Intelligence Lab. He left the U.S. for Canada partly because of his opposition to the politics of former president George W. Bush and the countrys military campaigns abroad.

Alphabet Inc. tapped him in 2017 to lead the companys AI research office in Edmonton through its DeepMind subsidiary, but shut it down in January as part of a company-wide restructuring.

The closing left Prof. Sutton with unfinished business, in a sense. His goal is to understand intelligence, as he puts it, a necessary undertaking if we are to build truly intelligent agents. His work at the university is one avenue to pursue that goal, as is his recent post with Keen Technologies, a U.S. AI startup founded by former Meta Platforms Inc. consulting chief technology officer John Carmack. Keen raised US$20-million last year, including from Shopify founder Tobi Ltke.

Openmind is one more way to pursue that goal, Prof. Sutton said. Although large language models, which power chatbots like ChatGPT, have garnered a lot of attention, he isnt particularly interested in them. Its a good, useful thing, but its kind of a distraction, he said.

He is far more interested in building AI applications capable of complex decision-making and achieving goals, which many refer to as artificial general intelligence, or AGI. I imagine machines doing all the different kinds of things that people do, he said. They will interact and find, just like people do, that the best way to get ahead is to work with other people.

Prof. Sutton will sit on the Openmind governing board along with University of Alberta computer science professor Randy Goebel and Joseph Modayil, who previously worked at DeepMind. Mr. Modayil is also Openminds research director.

Understanding the mind is a grand scientific challenge that has driven my work for more than two decades, he said in an e-mail.

A committee that includes Alberta Plan co-authors and U of A professors Michael Bowling and Patrick Pilarski will select the research fellows. Openminds research agenda will be set independently from its funding sources, according to a backgrounder on the institute provided by Prof. Sutton.

The briefing also notes that Openmind researchers will be natural candidates for founding startups and commercializing research outside the non-profit. Although there may be no legal obligation for an Openmind researcher to work with Openmind donors, familiarity, trust, and consilient perspectives would make this a likely outcome, according to the backgrounder.

The backing from Huawei puts the company in a better position to work with Openmind talent, Mr. Hinton said. Even though the research will be open-source, foreign multinational companies such as Huawei are often more equipped to capitalize on it than Canadian firms, which have a poor track record of protecting intellectual property and capturing the economic benefits that come with innovation.

Canadian governments review transactions involving foreign companies and physical assets, such as mines, to ensure the domestic economy benefits. But they fall short with IP. When it comes to intangible assets, we dont understand how that works, Mr. Hinton said.

Prof. Sutton is a big proponent of open-source and has a dim view of IP, saying that the focus on ownership can slow down innovation. You are interacting with lawyers and spending a lot of time and money on things that arent advancing the research, he said. It just doesnt seem like its worked at all for computer science IP.

He is open to more funding for Openmind and said that if donors are uncomfortable with Huaweis involvement they can also support AI research through the reinforcement learning lab at the University of Alberta. Openmind is adamant that Huawei cannot influence the non-profits research, he added, and said he would decline further funding if the company attempted to do so.

I see this as a purely positive and mutually beneficial way for Huawei and academic researchers to interact, he said. It may not last, but while it does, it is entirely a good thing.

Sam Altman was briefly ousted as the CEO of OpenAI, the result of infighting on the companys board of directors. For how long did the attempted corporate coup last?

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Top AI researcher launches new Alberta lab with Huawei funds after ... - The Globe and Mail

We are living in a world of probabilities. When I started talking about AI and its implications years ago, the most common question was is AI coming after us?

And while the question remains the same, my response has changed regarding probabilities. It is more likely to replace human judgment in certain areas, so the probability has increased over time.

As we discuss a complex technology, the answer will not be straightforward. It depends on several factors, such as what it means to be intelligent, whether we suggest replacing jobs, anticipating the timelines for Artificial General Intelligence (AGI), or identifying the capabilities and limitations of AI.

Let us start with understanding the definition of Intelligence:

Stanford defines intelligence as the ability to learn and perform suitable techniques to solve problems and achieve goals appropriate to the context in an uncertain, ever-varying world.

Gartner describes it as the ability to analyze, interpret events, support and automate decisions, and take action.

AI is good at learning patterns, however, mere pattern recognition does not qualify as intelligence. It is one of the aspects of the broader spectrum of multi-dimensional human intelligence.

As experts believe, AI will never get there because machines cannot have a sense (rather than mere knowledge) of the past, the present, and the future; of history, injury or nostalgia. Without that, theres no emotion, depriving bi-logic of one of its components. Thus, machines remain trapped in the singular formal logic. So there goes the intelligence part.

Some might refer to AI clearing tests from prestigious institutes and, most recently, the Turing test as a testament to its intelligence.

For the unversed, the Turing test is an experiment designed by Alan Turing, a renowned computer scientist. According to the test, machines possess human-like intelligence if an evaluator cannot distinguish the response between a machine and a human.

A comprehensive overview of the test highlights that though Generative AI models can generate natural language based on the statistical patterns or associations learned from vast training data, they do not have human-like consciousness.

Even advanced tests, such as the General Language Understanding Evaluation, or GLUE, and the Stanford Question Answering Dataset, or SQuAD, share the same underlying premise as that of Turing.

Let us start with the fear that is fast becoming a reality will AI make our jobs redundant? There is no clear yes or no answer, but it is fast approaching as the GenAI casts a wider net on automation opportunities.

McKinsey reports, By 2030, activities that account for up to 30 percent of hours currently worked across the US economy could be automateda trend accelerated by generative AI.

Profiles like office support, accounting, banking, sales, or customer support are first in line toward automation. Generative AI augmenting the software developers in code writing and testing workflows has already impacted the job roles of junior developers.

Its results are often considered a good starting point for an expert to enhance the output further, such as in making marketing copy, promotional content, etc.

Some narratives make this transformation sound subtle by highlighting the possibility of new job creations, such as that of healthcare, science, and technology in the near to short term; and AI ethicists, AI governance, audits, AI safety, and more to make AI a reality overall. However, these new jobs can not outnumber those being replaced, so we must consider the net new jobs created to see the final impact.

Next comes the possibility of AGI, which, similar to the multiple definitions of intelligence, warrants clear meaning. Generally, AGI refers to the stage when machines gain sentience and awareness of the world, similar to a human's.

However, AGI is a topic that deserves a post on its own and is not under the scope of this article.

For now, we can take a leaf from the diary of DeepMinds CEO to understand its early signs.

Looking at a broader picture, it is intelligent enough to help humans identify patterns at scale and generate efficiencies.

Let us substantiate it with the help of an example where a supply chain planner looks at several order details and works on ensuring the ones at risk of being met with a shortfall. Each planner has a different approach to managing the shortfall deliveries:

As an individual planner could be limited with its view and approach to managing such situations, machines can learn the optimal approach by understanding the actions of many planners and help them automate easy scenarios through their ability to discover patterns.

This is where machines have a vantage point over humans limited ability to simultaneously manage several attributes or factors.

However, machines are what they are, i.e., mechanical. You can not expect them to cooperate, collaborate, and develop compassionate relationships with the teams as empathetically as great leaders do.

I frequently engage in lighter team discussions not because I have to but because I prefer working in an environment where I am connected with my team, and they know me well, too. It is too mechanical to only talk about work from the get-go or try to act as it matters.

Take another instance where a machine analyzes a patients records and discloses a health scare as-is following its medical diagnosis. Compare this with how a doctor would handle the situation thoughtfully, simply because they have emotions and know what it feels like to be in a crisis.

Most successful healthcare professionals go beyond their Call of Duty and develop a connection with the patient to help them through difficult times, which machines are not good at.

Machines are trained on data that could capture the underlying phenomenon and create models that best estimate them.

Somewhere in this estimation, the nuances of specific conditions get lost. They do not have a moral compass, similar to a judge has when looking at each case.

To summarize, machines may learn patterns from data (and the bias that comes with it) but do not have the intelligence, drive, or motivation to make fundamental changes to handle the issues plaguing humanity. They are objective-focused and built on top of human intelligence, which is complex.

This phrase sums up my thoughts well AI can replace human brains, not beings.

Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.

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Will AI Replace Humanity? - KDnuggets

What could you do by your first birthday?

Chances are not much, beyond maybe walking and gurgling a word or two.

For generative artificial intelligence (AI), the level of development after just one year is an entirely different story.

The precocious innovation can alreadypredict the 10-day weather forecastbetter than current gold-standard meteorological systems across nearly all (90%) of the 1,380 key metrics measured.

And thats just within one domain. The innovative technology is making accelerated strides across all kinds of ecosystems.

From purpose-built products to service-as-software advances, this is the weekly pulse check on the top AI news and innovations PYMNTS has been tracking.

Generative AIs capabilities are advancing to the degree that many industry pioneers are returning to the initial sci-fi dream that spurred their interest in the AI field: developing artificial general intelligence (AGI), or software that matches human intelligence.

OpenAIs CEO Sam Altman shared on Monday (Nov. 13) that he plans tosecure additional fundingfrom Microsoft, which has alreadypledged $10 billionto his firm, tofund AGI development.

Magic intelligence in the sky. I think thats what were about, Altman said.

Following the news, PYMNTSreportedon how intelligence, even when encoded into a software tool, is a continuum, and how any future AGI systems will exist on that same continuum.

But all the buzz about AI accelerationism has some industry groups worried.

A group of more than40 venture capital (VC) firms, includingGeneral Catalyst,Felicis Ventures,Bain Capital,IVP,Insight PartnersandLux Capita, on Tuesday (Nov. 14) signed voluntary commitments around how the startups they back should develop AI technology responsibly as the technology and the companies behind it continue to grow.

Menlo Ventures, which is not among the commitments signatories,announcedon Thursday (Nov. 16) that it had raised $1.35 billion to invest in AI.

The VC-signed voluntary agreement is meant to demonstrate leadership from the private sector around controlling for AIs risks, but it hassparked a debate among AI founders around the line between responsible development and regulatory capture.

And it isnt just the VC community. TheNational Retail Federation(NRF) also released on Monday itsPrinciples for the Use of Artificial Intelligence in the Retail Sector, providing aframework for retailersto govern and strategically plan their use of AI.

Thats because retailers, both online and brick-and-mortar, are increasingly turning to AI to make the experience better and more streamlined for their customers.

In the midst of the holiday season, despite projections of weak consumer spending forecast amid an inventory glut, Macys announced Thursday that they havemoved to implement AIto better adjust its inventory based on holiday demand.

Elsewhere,Googleon Thursday addedpersonalized gift recommendationsto its generative AI capabilities in Search.

But it isnt just retail where AI is having an impact.

Airbnb on Tuesdayannouncedits first acquisition as a public company an AI startupcalled Gameplanner.ai. The news comes on the heels of Airbnb CEOBrian Cheskysaying that AI will help turn Airbnb into the ultimate travel agent and unlock opportunities weve never seen during his companys most recent earnings call.

And leading music services areincreasing their use of AI capabilities to offer more personalized experiences to drive consumer loyalty. For instance, SpotifyandGoogle Cloudannounced Thursday the expansion of theirpartnership, leveraging AI to drive engagement with the leading global music streaming platform.

Beyond the marketplace,OpenAIis reportedlylooking into waysto bring its popular generative AI ChatGPT chatbot into classrooms, exploring the educational applications of AI technology.

Thomson Reuterson Wednesday (Nov. 15) launched a series of initiatives aimed attransforming the legal professionthrough the use of generative AI.

This comes as PYMNTS Intelligence in The Confluence of Law and AI: An Inevitability Waiting to Happen, a collaboration withAI-ID, finds that more than half of legal professionals are uncertain about the technologys reliability, and nearly two in five do not trust it.

Consumers of legal services are notentirely won overeither, with 55% ofclients and potential clientsexpressing serious concerns about the use of AI within the legal profession.

Still, 62% of legal professionals believe thateffective use of generative AIwill differentiate successful firms from unsuccessful ones in as little as five years. An even higher share, 80%, agree thatgenerative AIwill introduce transformative efficiencies a sentiment echoed by law firms and corporate legal departments.

Those potential benefits transformative across not just law but all sectors are a part of why Chinese tech giantAlibabareportedly said Thursday it will not spin off its cloud intelligence business amid theongoing focus on AI.

Of course, the ongoing focus on AI doesnt come without some growing pains.

OpenAIis putting a hold on new signups for itsChatGPT Plusprogram, due to widespread demandputting a strain on the platform, the company said Tuesday, as tech companies increasingly rely on consumer subscriptions to boost profits.

TheFederal Trade Commission(FTC) also on Thursdaytook a proactive stancein protecting consumers from the potential dangers of artificial intelligence-enabled voice cloning technology, unveiling theVoice Cloning Challengeand inviting submissions of ideas that can help prevent the misuse of AI technology for fraudulent and malicious purposes.

In an unrelated announcement,Microsofton Wednesday launched the public preview release ofAzure AI Speech, technology that allows users tocreate talking avatar videoswith text input and build real-time interactive bots using human images.

AndAmazonintroduced on Wednesday anAI security solution designed to cater to the needs of small- to medium-sized businesses (SMBs) that combines robotics, smart security and AI to enable customers to keep an eye on their business 24/7, even from home.

But for AIs widespread applications to be fully capitalized on, end-users need to be familiar with the technology.

New PYMNTS Intelligence from AI-Enabled Payments Enhance Customer Options, a report by PYMNTS Intelligence andACI Worldwide, found that there are significant differences across gender, generations and income levels when it comes to familiarity with AI.

For example, a higher percentage of men 46% consider themselvesvery or extremely familiar with AIcompared to 33% of women. Similarly, although the margin is smaller, a higher percentage of men (35%) than women (about 31%) acknowledge AIs significant role in their daily personal activities.

For all PYMNTS AI coverage, subscribe to the dailyAI Newsletter.

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This Week in AI: Accelerationism, AGI and the Law - PYMNTS.com

November 24, 2023

By Kevin Armstrong

Elon Musk announced earlier this month that Tesla's Full Self-Driving (FSD) v12 would be released in two weeks. The usual timeframe reference Musk is famous for was met with skepticism. However, it seems that Tesla is right on track with its rollout.

We have learned through a trusted source that FSD v12 has started rolling out internally with Tesla update 2023.38.10.

Update: Musk has responded to our article on X, confirming that Tesla has indeed starting rolling out FSD v12 to employees.

FSD v12 is the update that is expected to remove "beta" from the title. The initial rollout to employees appears more limited in scale than previous updates. Considering the magnitude of the changes in this version, it makes sense to start slow.

The timing of this internal release is close to two major Tesla events. The Cybertruck delivery event is just a few days away. Many eyes will be on the company during the event, allowing Tesla to possibly show the world its latest breakthrough. Alternatively, the highly anticipated holiday update, often regarded as the best update of the year, is expected to be released by 'Santa Musk' in the coming weeks, potentially featuring v12 as a significant addition.

The latest public FSD build, v11.4.7.3, is Tesla update 2023.27.7. This FSD build is several revisions behind the latest production builds, so it's nice to see that v12 will bring FSD beta testers back up to speed with some of the latest Tesla features such as Predictive Charger Availability, Faster Hazard Lights After a Crash, and other features included in updates 2023.32 and 2023.38.

As for FSD improvements, we haven't had a chance to see the release notes for FSD v12 yet. However, now that it has started going out to employees, it shouldn't be long before we find out all the FSD improvements included in this milestone release.

A significant change in v12 is eliminating over 300,000 lines of code previously governing FSD functions that controlled the vehicle, replaced by further reliance on neural networks. This transition means the system reduces its dependency on hard-coded programming. Instead, FSD v12 is using neural networks to control steering, acceleration, and braking for the first time. Up until now, neural networks have been limited to detecting objects and determining their attributes, but v12 will be the first time Tesla starts using neural networks for vehicle control.

The FSD v12 represents a significant leap in Tesla's FSD technology. Musk has described it as an "end-to-end AI", employing a "photon in, controls out" approach akin to human optical processing. This analogy underscores Tesla's ambition to replicate human-like decision-making capabilities in its vehicles.

Labeled as a "Baby AGI" (Artificial General Intelligence), the system is designed to perceive and understand the complexities of the real world. This philosophical and technological shift in AI-driven autonomy was vividly showcased during a live-streamed drive by Musk through Palo Alto, where the Model S demonstrated smooth and almost flawless navigation through various real-world scenarios, including construction zones, roundabouts, and traffic. That was three months ago; imagine how much the system has learned in 90 days.

The rollout of FSD v12 marks a critical point in Tesla's journey in AI and autonomous driving. It's not just about technological prowess but also about aligning AI with nuanced human behavior. With Musk's continued focus on AI, which is evident across his ventures, Tesla remains a crucial player in the EV market and the broader AI revolution.

As we await further details on the public release of FSD v12 and its potential showcase at the Cybertruck event, it's clear that Tesla is moving closer to a future where cars are not just self-driving but are also intelligent and responsive to the complexities of the real world.

Subscribe to our newsletter to stay up to date on the latest Tesla news, upcoming features and software updates.

By Kevin Armstrong

Tesla's highly anticipated Cybertruck is gracing showrooms nationwide. Cybertruck was trending on X as users posted pictures and videos from Tesla stores throughout the U.S., ramping up even more excitement for the delivery event on November 30th.

Cybertruck started its showroom appearances in San Diego and San Jose earlier this week, but according to Elon Musk, several more Tesla stores may want to clear some space. Musk posted on X: "Cybertrucks are on their way to Tesla stores in North America!" It's unclear if that means every Tesla store and gallery across North America or just a few. There are 236 stores in the U.S., 24 in Canada, and 3 in Mexico.

It's also strange that so many Cybertrucks are in showrooms, as it's been reported that Tesla Product Design Director Javier Verdura said only ten would be delivered at the November 30th event. It's believed that slow rollout highlights the company's cautious approach, ensuring quality control before increasing deliveries and production volumes.

'A Better Theater,' a popular site for Tesla owners to stream content in their vehicles, is tracking all showrooms which have the Cybertruck on display. We've added the list below, but for the latest locations, checkout their site.

860 Washington St., New York, NY 10014

333 Santana Row, San Jose, CA 95128

6692 Auto Center Dr, Buena Park, CA 90621

4545 La Jolla Village Dr, San Diego, CA 92122

Bellevue, WA 98004 (Coming Soon)

2223 N Westshore Blvd, Tampa, FL 33607

4039 NE 1st Ave, Miami, FL 33137

9140 E Independence Blvd, Matthews, NC 28105

901 N Rush St, Chicago, IL 60611

This widespread showcase in Tesla showrooms is more than just about displaying the new Cybertruck; it's a strategic move to draw consumers into showrooms. As Cybertrucks make their way into more stores, potential customers and enthusiasts get a firsthand look, creating a tangible sense of excitement. This strategy is particularly effective before Black Friday, leveraging the shopping season's foot traffic to draw more attention.

Adding to the intrigue, Tesla has revealed key specifications of the Cybertruck in its showrooms. The confirmed towing capacity of 11,000 lbs and a payload of 2,500 lbs have been significant talking points, giving potential buyers more reasons to consider the Cybertruck as a formidable competitor in the electric vehicle market. However, we still don't know the price.

Despite the initially limited delivery numbers, Tesla's decision to place Cybertrucks in showrooms across North America is another clever marketing move - for a company that doesn't advertise. It maintains high levels of interest and anticipation and gives the rest of the lineup a chance to shine. Christmas comes earlier this year; just a few more sleeps until November 30th.

By Kevin Armstrong

Tesla's incredible journey started by piecing together the Roadster, a painstaking ordeal that nearly caused the company to go bankrupt more than once. The piece-by-piece instruction manual to build the car that started an automotive revolution has been made public, fully open-sourced. CEO Elon Musk posted on X: "All design & engineering of the original @Tesla Roadster is now fully open source. Whatever we have, you now have."

The open-source announcement has sparked enthusiasm and curiosity within the engineering community. A post from the World of Engineering (@engineers_feed) on X, asking, "Does this mean I can build my own roadster in my garage?" garnered a direct response from Musk: "* some assembly required."

Theoretically, if one can get their hands on the parts, they have some direction to build one of these historic vehicles. From a business side, this kind of information sharing with competitors is curious, although it does follow Tesla's mission statement to accelerate the world's transition to sustainable energy. Although the information is 15 years old, it could provide some useful information.

Tesla has clarified the nature of the information released, stating it's a resource for Roadster enthusiasts derived from the car's R&D phase. The details are not intended for manufacturing, repair, or maintenance and may not align with final production models. Users leveraging this information are reminded of their responsibility to adhere to legal and safety protocols, as Tesla offers no warranties for work done using these details. This open-source initiative encourages innovation but stresses the importance of safety and legal compliance.

Launched in 2008, the original Roadster was the first legal electric vehicle on highways to utilize lithium-ion batteries and achieve over 200 miles per charge. It bankrolled the next phase of Tesla, the Model S, and set a benchmark for future EVs.

While this open-source initiative revisits Tesla's past, it also shifts the focus back to the next-generation Roadster. Initially unveiled in 2017, its production has been delayed, and there is no timeline for when the new sportscars will be manufactured. Moreover, Tesla's focus on the Cybertruck and a more affordable $25,000 EV indicates a strategic balance between innovation and mass EV adoption.

Tesla's decision to make the original Roadster's design and engineering open source should not be too surprising. Musk has said, "I don't care about patents. Patents are for the weak. They don't actually help advance things. They just stop others from following you." Perhaps the biggest surprise is how long it took for Musk to open-source the Roadster blueprint.

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Tesla FSD v12 Rolls Out to Employees With Update 2023.38.10 ... - Not a Tesla App