AI company raised 15 million US dollars, but it attracted LeCun to "ridicule the three companies" and publish a book to ridicule the CEO?

 An AI start-up company just raised 15 million US dollars. The founders all have a good face. Others congratulate and talk about each other with business, but they unexpectedly attracted the ridicule and irony of the Turing Award winner.

The sarcasm is Yann LeCun, one of the three famous deep learning giants.

And LeCun is not someone else but his New York University colleague Gary Marcus, who is not known for criticizing AI and deep learning.

This Professor Marcus is no stranger to people who pay attention to AI, and even gave him the nickname "Sniff" Marcus.

But this time, when he was the founder and CEO of the AI ​​company's financing rejoicing, he received a rude ridicule from LeCun.

Marcus uses deep learning to finance, LeCun is very mindful.

What is Marcus' startup company doing?

Why did LeCun take the initiative to come to taunt?

Let me introduce the company that Marcus founded.

Robust.AI, June 2019, was co-founded by familiar Gary Marcus, MIT professor Rodney Brooks, and several other scientists who study robotics.

Among them, Marcus and Rodney Brooks, as CEO and CTO respectively, are the leaders and core of the new company.

Rodney Brooks is also a bigwig in the field of AI and robotics, and was the director of the MIT Artificial Intelligence Laboratory.

The goal of Robust.AI is to make a set of "robot cognitive engines" that can be used in many industrial scenarios.

In layman's terms, this is a fight made of soft pieces of the system of the company.

What they are doing is the AI ​​algorithm of industrial robots, which can not only meet specific scenarios, but also surpass current products in terms of "cognition" of the environment, so that they can be quickly deployed on different production lines to achieve "universal".

Robust.AI currently has 25 employees and job advertisements have been running. According to Marcus introduced to Forbes magazine, they have already cooperated with a certain customer and expect to deliver the first batch of products in 2021.

Two days ago, foreign media reported on Robust.AI’s latest US$15 million financing. Together with last year’s seed round of US$7.5 million, a total of US$22.5 million has been raised.

All the above information is normal, and there is nothing outrageous.

However, LeCun couldn't stand it.

You Marcus criticized AI for not working, poor deep learning cognitive ability, and opacity, and won fame and attention...

Now the other side uses this to start a company to raise funds to "circulate money"?

It's really interesting .

"True Fragrance" Marcus

"You shouldn't use deep learning? Gary (referring to Marcus) said that deep learning doesn't work."

Under Robust.AI's financing news, LeCun asked "there is something in the word".

After all, Marcus's previous criticism of deep learning was really sharp and unrelenting, and it was simply anti-deep learning.

However, Marcus was different this time and did not directly challenge.

The one who responded to LeCun in the Facebook conversation was the CTO of Robust.AI and Rodney Brooks, a professor at the MIT Matsushita Robot Research Institute.

Brooks answered honestly: "We did use deep learning techniques."

△ The big guys quarreled under the news comments

But he further explained that this is not all the company's technical solutions.

In addition to deep learning, there are technologies such as control theory, SLAM (simultaneous localization and mapping), IK solver, etc., and there is no stick to any genre.

"Oh, what you are doing is what everyone else is doing."

LeCun continued to make up for the knife-there is no innovation or difference, I thought that the criticism was so strong, maybe it really you can you up.

Of course, if you don’t understand Marcus’s previous AI criticism-the core is deep learning criticism, you may think that the Turing Award winner, how to "be careful"...

But a few years ago, Marcus was really merciless.

Teacher Ma not only spared no effort to criticize deep learning, saying that it is "poor in perception" and "too dependent on data"...

Every time there is a major progress in the AI ​​field, there is almost a rush to pour cold water, such as criticizing GPT-3 some time ago.

Sometimes it even seems to be a bone in the egg, such as GPT-3. Compared with the previous two generations, the effect is not so good.

But Marcus can always criticize the shortcomings of the current version, saying that this AI cannot be used in fields such as medical care, and there are many problems in the long tail scene...To be honest, it does seem to be criticized for criticism.

So now, the company he founded by himself uses deep learning. Although it also solves real problems, it still focuses on financing and burning money...

It can be regarded as inevitable "the law of true fragrance", and he has become his own criticism.

Regarding Yann LeCun

This is probably understandable, why Yann LeCun is so upset.

After all, what Marcus is opposed to is his most important academic achievement. It is also the revival that he and Hinton, Bengio and others have been sitting on for more than ten years. He knows that AI is not easy to usher in.

They naturally know the limitations of deep learning-driven AI, so they have been constantly looking for breakthroughs and opening black boxes...it is the constructive group.

In fact, LeCun did not respond much to Marcus's previous criticism.

But this time, it can be regarded as the best opportunity, and LeCun is "knowingly asking"-in Forbes' report on Robust.AI financing, it has already stated the use of deep learning technology.

LeCun put it plainly, just want to ridicule this behavior of smashing the pot and eating.

Before replying to "Similar to others", LeCun also wrote a long paragraph satirizing Marcus, to the effect:

I'm trying to write an academic monograph, and I'm sure this book is more offensive than anything Marcus said...

Regardless of whether you are engaged in AI, or CS, neuroscience... After reading, you can refresh the major knowledge in this field that you have never heard of...

There will be no progress in these areas for centuries...I look forward to people reading blood pressure rise.

Hahahaha...

LeCun really "keep grudges" and put "neuroscience" in it.

Because Marcus is a professor of psychology and neuroscience-when there was a serious conflict before, some people criticized Marcus for being "unqualified" to criticize things that were not his major.

Another Gary joins the debate

LeCun's irony, Gary Marcus as the victim has never responded...

Instead, another Gary came out to argue with LeCun.

Gary Bradski is also a well-known leader in the field of machine learning. The most widely known work is the OpenCV computer vision software library.

He meant to say:

LeCun, wait a moment, I, Gary, have something to say. I think deep learning is a revolutionary advancement and a practical tool.

Everyone is now using differential methods for model rendering and simulation. This is very important because everyone adds these elements as variables to Pytorch.

A piece of code can be differentiated with a little modification, which makes deep learning using Pytorch progress rapidly.

Deep learning partly proves the feasibility of general artificial intelligence, but the problem is the lack of interpretation and attribution standards for the results, but few people question it.

I wish the company a success! I wish you always have clear and usable gradients.

Gary Bradski is much more euphemistic, acknowledging the importance of deep learning at the moment, but saying that the model cannot explain it.

He uttered the current status and hidden worries of deep learning. This is the crux of Marcus's long-term bombardment of deep learning and his grievances with various bigwigs. It is also the focus of debate on the future development path of artificial intelligence .

What is the "wrong" of deep learning?

Regarding Marcus's criticism of AI, the most representative one is his long article written in 2018, which comprehensively and systematically explained his views on deep learning.

This article can be summarized as the top ten flaws of deep learning.

1. Deep learning is highly dependent on data

Humans rely on clear definitions to learn abstract relationships easily.

However, deep learning does not have the ability to learn abstract concepts through clear definitions of language description, and requires hundreds of millions of data training at every turn.

Geoff Hinton also expressed concerns about the deep learning system's reliance on large amounts of labeled data.

2. Deep learning transfer ability is limited

The "deep" in "deep learning" refers to the technical and architectural nature, that is, many hidden layers are stacked. This kind of "deepness" does not mean that it has a deep understanding of abstract concepts.

Once the task scene changes, you need to find data training again.

3. Deep learning can't handle the hierarchical structure naturally

Most current deep learning-based language models treat sentences as sequences of words.

When encountering an unfamiliar sentence structure, the Recurrent Neural Network (RNN) cannot systematically display and expand the recursive structure of the sentence.

This is because the correlation between the groups of features learned by deep learning is flat.

4. Deep learning can't handle open reasoning

On the Stanford question and answer data set SQuAD, if the answer to the question is contained in the title text, the current machine reading and comprehension system can answer it well, but if it is not in the text, the system performance will be much worse.

In other words, the current system does not have the same reasoning ability as humans.

5. Deep learning is not transparent enough

The "black box" nature of neural networks has always attracted much attention. But this issue of transparency has not been resolved so far.

6. Deep learning has not been combined with prior knowledge

Because of the lack of prior knowledge, it is difficult for deep learning to solve open problems, such as how to repair a bicycle with a rope entangled in the spokes?

Seemingly simple questions involve a large number of different knowledge in the real world, and no data set is suitable for them.

7. Deep learning cannot distinguish between cause and effect and related relationships

Deep learning systems learn the complex correlation between input and output, but cannot learn the causal relationship between them.

8. Deep learning needs to work in a strict and stable environment

The logic of deep learning works best in a highly stable environment. For example, in a game like chess, the rules will not change, but in political and economic life, there is only change.

9. Deep learning is only an approximation

Deep learning performs well in some specific areas, but it is also easy to be fooled.

10. Deep learning is difficult to engineer

Deep learning is difficult to achieve robust engineering, because it is difficult to ensure that the machine learning system works effectively in a brand-new new environment.

These ten "criminals" can be described as cutting-edge. Deep learning does have problems such as mechanical dependence on training, opacity, and fuzzy causality.

However, if Marcus's sharp criticism is limited to academics, there may not be LeCun's brooding.

Deep learning is currently the hottest tool in the AI ​​field.

Marcus bluntly said that the academia's hype about deep learning has been too much and objectively misled people's understanding of the development path of AI.

These issues have become the fundamental starting point for Marcus to bombard deep learning and question the progress of AI.

Over the years, Marcus has repeatedly confronted the bigwigs of the AI ​​industry, including the Big Three in deep learning, Wu Enda and so on.

The defense of the big guys focused on "deep learning is not to define a thing, but to point out a direction."

It doesn't make sense to ask if deep learning can be done. It makes sense to ask how to train it to do it.

Deep learning is a kind of thinking and methodology.

Obviously, AI bigwigs do not want to deny deep learning from the root, otherwise AI will fall into silence and the dark night of history.

In the face of Marcus’s “qualitative” questioning, only Thomas G. Dietterich, the former chairman of AAAI, responded after a debate:

"The contribution or criticism of deep learning does not come from results."

In addition, Marcus even had a face-to-face debate with LeCun: "Is it necessary for AI to have human-like capabilities".

The essence of this debate is a series of flaws in deep learning.

Marcus believes that such AI will not go far, and LeCun believes that it has great potential and does not need to have brain-like capabilities to meet demand.

Of course, they couldn't convince each other at all.

On the one hand, deep learning is invincible and plays a key role in many AI research. Some more mature technologies have actually been implemented. The capabilities that deep learning has demonstrated cannot be replaced by other methods.

On the other hand, the limitations and defects of deep learning are indeed difficult to crack in a short time.

So obviously, this is a battle between the "revolutionaries" and the "reformers".

The "revolutionaries" represented by Marcus denied all-round and reassessed deep learning and AI...

LeCun and others have finally used deep learning to promote the AI ​​renaissance. Even though they are well aware of the shortcomings of deep learning and the current challenges of AI, they still hope to continue to find countermeasures in the development, rather than waste food due to choking...

It's just that Marcus, the "revolutionary", just overthrows, doesn't care about construction, and now uses the technology he criticized to finance.

LeCun didn't say swear words, he was probably restrained enough.

What do you think?

Subvert Excel through Lego, no-code productivity tool "Treelab" completes two consecutive rounds of preA and preA+ financing

 the no-code productivity tool "Treelab" (www.treelab.com.cn) announced that it had won the PreA round led by GGV Jiyuan Capital; recently it completed the investment led by Morningside Capital and GGV Jiyuan Capital. PreA+ round with Mingshi Capital's follow-up investment. Two consecutive rounds of financing within six months, the total amount is close to 10 million US dollars.

"Treelab" benchmarked against the US codeless custom SaaS software Airtable, and this benchmarking awareness and understanding of industry pain points are derived from the past practice of "Treelab" founder and CEO Ricky: When Ricky was 15 years old, he found the factory at home Although there is an ERP system, it is difficult to synchronize between the systems and still rely on the traditional form of exporting Excel. After discovering this pain point, he began to build a new platform to automatically synchronize data from different systems.

Although it does allow data to circulate, the software must continue to meet demand and be constantly modified. This is also a problem faced by many SaaS companies, that is, the balance between product standardization and customization of customer needs.

The shock to Ricky was that when he planned to sell the platform to a brand in the United States, he found that the Airtable template could achieve the functions he had built for two years.

The industry is complex, and the needs are constantly changing, but the codeless custom platform represented by Airtable actually turns the lowest part of the software, that is, the addition, deletion, modification, and checking of software into a user experience mechanism. More complex services can be solved through external methods such as API.

At present, most of those engaged in this industry in China are from the perspective of "facilitating the boss management process". "Treelab" cuts in from the perspective of "bringing convenience to actual business operators".

Of course, this may bring to ask questions, after all payers are the boss, they do not actually care about the convenience of the user. In this regard, Ricky has his own views: First, the data can already generate value after circulation, which is meaningful to the boss; second, the direct customer groups of "Treelab" are small companies such as big C, small B, etc. The business team may only have a few or even one person. He is both the boss and the employee, so "Treelab" is more valuable to him.

Of course, in addition to the standardization of small businesses directly, "Treelab" has not given up the market for medium and large customers. But the "Treelab" approach is not to directly go to Big B, but to cooperate with other customized industry solutions and be wrapped in it.

Currently, "Treelab" can serve the following work scenarios:

• Customer relationship management: For domestic small businesses, the immediate pain point is not managing customers, but how to obtain business opportunities. "Treelab" cooperates with data companies to provide companies with lead management capabilities.

• Project management: Display the overall project in a single view, and assign tasks to project tracking, which is more intuitive and easier to collaborate.

• Questionnaire survey: While simply collecting data, you can also perform one-stop data storage and analysis.

In summary, "Treelab" is a lightweight way to solve problems that Excel cannot solve in Excel usage scenarios. Behind this is the core competitiveness of "Treelab"-the bottom layer is the database, and the middle is the middle stage. 90% of them are general-purpose capabilities and 10% are customized; therefore, if the underlying and general-purpose capabilities are set up, it is enough Build different lightweight applications faster.

 "Treelab" currently serves industries including but not limited to supply chain, Internet, consulting, finance, design, advertising, etc. Its customers include well-known companies such as Xiaomi, Greenland Group, Nestlé, Chenguang, Decathlon , SGS, etc.

Ricky believes that the market "Treelab" will face in the future will exceed 100 billion. The market is composed of three parts. One is the stock market, that is, the office field that has already been digitized; the second is the incremental market. For many vertical industries that no one cares about, you can build your own templates for your industry, such as China’s 500 film productions. The company's total IT budget is only a few hundred million, which is not enough to support a vertical software, but you can use "Treelab" to customize your own templates; third, for the office market and Excel usage scenarios, you can use "Treelab" to be smarter and more convenient Have to be replaced.

"Treelab" was founded in 2019, and its team members come from well-known companies such as Tencent, Alibaba, Teambition, Tezan, and Graphite Document . This round of financing will help the team further promote product iteration, function expansion, and improve the sales and support team.

Profitable for three consecutive years, global cloud communication service provider "Instant Technology" received US$50 million in Series C financing

 the global cloud through information service providers "shall constitute Technology" get fifty million dollars C round of financing, Tencent lead investor, existing shareholders to continue with the vote. After this round of financing, "JiZhou Technology" will continue to strengthen the construction of the platform, create more efficient and professional platform service standards, launch more diversified products and services, and more accurate and close to user needs. Program.

"Instant" was established at the end of 2015. The founder and CEO Lin Youyao and the four co-founders are all from Tencent. The core technology team is from Tencent, Huawei, Huanju Times and other companies. The services provided cover PaaS products that can only be accessed with a high amount of code. APaaS products that can be accessed with low code and SaaS products with zero-code access provide enterprises with real-time audio and video interactions such as interactive live broadcast, online classrooms, telemedicine, conferences, games, finance, smart devices, and government-enterprise collaboration ability.

Customers can flexibly choose to use different functional modules to assemble and combine them into a personalized solution that conforms to business operation logic.

The technical advantage of "Instant Technology" comes from the self-developed RTC engine and MSDN network. The former ensures that "instant construction" is not restricted by third-party technology and meets the individual needs of users; the latter maximizes the balance between cost, service efficiency and quality of the overall service.

The essence of the To B industry is to create long-term value for customers, but the difficulty is to earnestly serve the different needs of each company. This is also another major advantage of "Instant" besides technology, that is, the emphasis on customer service. Since its establishment 5 years ago, the company has been positioning "technology + service" as its corporate positioning, insisting on the logic of customer service.

With the blessing of these two major advantages, "Instant Technology" has developed rapidly during the epidemic period when real-time audio and video interaction scenes were rapidly activated. Relying on the service advantages of the self-built MSDN network, it once again won the trust of customers. The average daily voice and video communication service time of the structure also quickly exceeded 2 billion minutes.

It is reported that for more than five years, the churn rate of major customers of "Instant Technology" has been almost zero, the annual churn rate of all customers is less than 3%, and the customer repurchase rate has reached 97%. Currently, more than 70% of the industry's leading customers are customers of "instant" real-time audio and video services. "Instant Construction" has been profitable since 2018. In 2019, the business has maintained steady growth, with a certain increase in business scale and profit. In 2020, it is expected that revenue growth will be close to 100%, achieving three consecutive years of profitability.

Ye Guantai, partner of Qiming Venture Capital, the B-round investor of "Instant Technology", believes that the business model of "Instant Technology" is worth learning from more companies: "The all-cloud infrastructure leads to basic operating cost investment and business scale that can be maintained from the beginning. In a reasonable proportion."

The cloud communication service industry is entering an explosive period, and this is an overall rapidly growing market. 36 krypton has also reported on the audio and video conference track . Due to the epidemic, the demand for multi-person, online, and instant messaging has skyrocketed. The market research report released by Zion Market Research shows that the market size of online real-time access will reach 21.023 billion US dollars in 2025, and the compound annual growth rate from 2019 to 2025 is 43.6%.

Talking about the future, CEO Lin Youyao said: "Next, "Instant Construction" will polish out various combination and innovative scenario solutions. "

Provides fully automatic AI middle and Taiwan SaaS service, "Deep Intelligence" completes tens of millions of angel round financing

 the fully automated AI mid-Taiwan provider " Deep Wisdom " has recently completed tens of millions of yuan in angel round financing. The investor in this round is Meihua Venture Capital, and the funds from the financing will be used for product development and market expansion. This is the company's second financing in a year.

"Deep Intelligence" was established in 2019 and is committed to the algorithm research and commercial scenarios of MetaAI technology (AI made by AI), providing full automation for B-end customers in retail/shoes and apparel, industrial manufacturing, trading platforms, finance and other industries AI middle-station SaaS service aims to reduce the labor cost of AI application landing.

 Deep Intelligence Fully Automatic Machine Learning Technology

Relying on the "Deep Intelligence" fully automatic AI middle station, customers only need to connect all data to the cloud API, and the middle station system will automatically generate a complete AI solution based on the customer's input data and generate automatic deployment codes.

At present, "Deep Intelligence" has completed the development of multiple core modules in the SaaS platform, which can realize full-modal and full-scenario AI services, and has been used for multiple industries such as retail/shoes and apparel, industry, trading platform, and finance. The head customer provided corresponding services. Customer data shows that the entire solution can greatly improve the effects of tasks such as decision-making, recommendation, and search, and increase business indicators by 40% to 60%, and achieve more than 60% labor cost savings and significant net profit improvement for customers.

When asked about the potential and value of fully automated AI mid-stage, Wu Chenglin, CEO of DeepFaith, briefly described the advantages of AI-enabled retail scenarios: "Although the retail industry has undergone multiple rounds of transformation and upgrades in recent years, cost management in the industry is difficult. , sales growth bottlenecks, supply chain efficiency and low polycyclic section core pain points still exist. "it also represents, AI can help and solve the product of intelligent design, sub-store sales forecast, pain points, multiple scenes of smart pricing, wisdom stores, etc., Upgrade and transform the retail industry as a whole. Multi-scenarios help retailers to improve overall supply chain efficiency, increase GMV and increase profits play a key role. "Deep Smarter" products can currently achieve the combined application of multiple retail scenarios, helping retail companies to reduce costs and increase efficiency.

In addition, "Deep Intelligence" can overcome the bottlenecks of traditional AI products with long landing cycles, long iterations of effects, and low input-output ratio. Wu Chenglin introduced that traditional AI development requires a complete AI team's design-development cycle of more than one year, and "Deep Intelligence" based on MetaAI technology can shorten the design-development cycle to less than one day, greatly reducing AI development costs; data shows , "Deep Intelligence" surpasses Google's corresponding products by 63.4% on average in multiple tasks, and can help customers obtain excess decision-making and distribution benefits.

At the same time, in the past year, "Deep Intelligence" won several international competitions (automatic deep learning direction) world championships, and stably won the first place on dozens of blind test data sets of different scenarios where AI landed. It also proves to a certain extent the universal applicability of "Deep Intelligence" products in different scenarios.

2020/04 Deep Intelligence won the world championship in the finals of the NeurIPS-AutoDL series competition

In terms of market trends, my country is currently accelerating the construction of new infrastructure. As one of the seven key areas, the artificial intelligence industry is ushering in a period of rapid growth in applications. "Deep Intelligence" also hopes to contribute to the realization of the intelligent development of the industry by providing enterprises with fully automated AI mid-stage products.

At present, the "Deep Intelligence" team has attracted first-class talents from 985/211 colleges and universities, top overseas colleges, including Stanford, CMU, Oxford, IC, UCL, Tsinghua University, Peking University, Xiamen University, etc., including more than ten A world champion; it brings together senior researchers and architects from Tencent, Google, Baidu , Huawei and other companies, and has experience in AI implementation with billions of users and hundreds of billions of data. In the future, "Deep Intelligence" will continue to improve MetaAI technology and continue to build fully automated AI middle stations to provide customers with better products and services.