AI Aliya Prokofieva

Blockchain in Space 2: Fear is the Path to the Dark Side

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What blockchain technologies have better chances to be applied in outer space
Blockchain in Space 2: Fear is the Path to the Dark Side

Today's technology is at the peak of cosmic fashion. Numerous visionaries and savvy analysts arrange ritual dances with tambourines around them, declaring Blockchain as the main catalyst for the forthcoming scientific and technological breakthroughs in space.

Most of these statements are from the evil one. Sooner or later the excitement will drop and it will become evident that the Blockchain is not a panacea capable of solving all the problems of the cosmic industry in one single stroke.

It is nothing more than an applied technology, created for the solution of narrowly specific problems. At the same time, one can not but admit that:

a) Blockchain copes quite successfully with its tasks

 

b) due to this it has all the chances to get accustomed to space seriously and for a long time

From the perspective of applying blockchain technologies in outer space, in my opinion, the most realistic are the following three:

Cryptocurrency

In space, the Blockchain continues to work for its intended purpose, now it is based on the creation and maintenance of cryptocurrencies with satellite hosting.

They do not have a binding to Earth, they can not reach ‘Big Brother.’ This money lives in space and, most importantly, works there it is invested in space research and development.

Research

Blockchain as a means of creating a secure information chain is ideally suited for fixing the results of research programs: observations of space objects, interplanetary expeditions, distant missions.

In the event of disputes, such an infochain may become an indisputable proof of the authorship of the discovery or priority of achievement.

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The military

The keyword here is "decentralization." For example the conditional grouping of military satellites. If it is controlled from a single center, it is vulnerable- it is enough to destroy this center in order to turn satellites into a useless pile of metal and plastic.

What if the algorithm of the group reaction to these or other threats is registered in the Blockchain and is maximally decentralized? No, point strikes will not help here.

By the way, it is likely that block technologies are already in full use in the military-space industry. But for obvious reasons, no one will tell us about this.

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Blockchain in Space 2: Fear is the Path to the Dark Side

🤖 AI
What blockchain technologies have better chances to be applied in outer space
Blockchain in Space 2: Fear is the Path to the Dark Side

Today's technology is at the peak of cosmic fashion. Numerous visionaries and savvy analysts arrange ritual dances with tambourines around them, declaring Blockchain as the main catalyst for the forthcoming scientific and technological breakthroughs in space.

Most of these statements are from the evil one. Sooner or later the excitement will drop and it will become evident that the Blockchain is not a panacea capable of solving all the problems of the cosmic industry in one single stroke.

It is nothing more than an applied technology, created for the solution of narrowly specific problems. At the same time, one can not but admit that:

a) Blockchain copes quite successfully with its tasks

 

b) due to this it has all the chances to get accustomed to space seriously and for a long time

From the perspective of applying blockchain technologies in outer space, in my opinion, the most realistic are the following three:

Cryptocurrency

In space, the Blockchain continues to work for its intended purpose, now it is based on the creation and maintenance of cryptocurrencies with satellite hosting.

They do not have a binding to Earth, they can not reach ‘Big Brother.’ This money lives in space and, most importantly, works there it is invested in space research and development.

Research

Blockchain as a means of creating a secure information chain is ideally suited for fixing the results of research programs: observations of space objects, interplanetary expeditions, distant missions.

In the event of disputes, such an infochain may become an indisputable proof of the authorship of the discovery or priority of achievement.

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Piglet’s Big Brother: How AI Changes Agriculture

The military

The keyword here is "decentralization." For example the conditional grouping of military satellites. If it is controlled from a single center, it is vulnerable- it is enough to destroy this center in order to turn satellites into a useless pile of metal and plastic.

What if the algorithm of the group reaction to these or other threats is registered in the Blockchain and is maximally decentralized? No, point strikes will not help here.

By the way, it is likely that block technologies are already in full use in the military-space industry. But for obvious reasons, no one will tell us about this.

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Waiterless in Seattle: How AI Robot SOTA Works in a Restaurant

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Food delivery company Plenty USA, Inc. is going to launch the latest version of the Japanese AI communication robot SOTA to a Seattle restaurant in the coming weeks
Waiterless in Seattle: How AI Robot SOTA Works in a Restaurant

Food catering company Plenty USA is going to launch the latest version of Japanese AI communication robot SOTA to a Seattle restaurant in the coming weeks.  

The company said the new SOTA robot will serve customers at JUNKICHI, a robatayaki izakaya restaurant in Seattle’s Capitol Hill neighborhood starting April 15. The earlier version of this robot has been tested at a Santouka Ramen place in the University Village.  

What SOTA looks like

Image: Yoshikazu Tsuno, AFP via Mashable
Image: Yoshikazu Tsuno, AFP via Mashable

Colored in blue, white and orange, SOTA is shaped like a cute creature. According to Nikkei Asian Review, SOTA weighs about one kg, and it is portable even for the elder people. At 28 cm high, the robot is suitable to put it on a table or a desk to talk to people. It priced around 100,000 Yen ($805).

How SOTA works in a restaurant

The new SOTA will sit on the table tops and there is a camera in its head, which connects images to Microsoft Azure’s FaceAPI service to allow SOTA to recognize human faces. The face-recognition technology enables the robots to remember repeat guests.

According to the company, SOTA is designed to communicate with customers. Customers can also use a smartphone app to make SOTA speak, and it can be told to have a special conversation with you through Microsoft Azure’s Text to Speech service, which means SOTA can talk with your table in real-time.

Using a robot is not a common thing at a restaurant in Japan, facing this trend, more and more kitchen robots and AI machines are creating to deliver food and help servers at restaurants in the US.

According to Market Insider, the attraction of the SOTA robot brings special reservations from customers who are willing to see the robot in action, so the restaurant reported a 10 percent increase in sales since it started using the AI robot. The company is excited to see how things go in the US market.

Read our article on how AI is used in agriculture, the industry that is increasingly on the cutting edge of new gen tech.

bitcoin

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Combination of Blockchain, AI Could Create Massive Synergies

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Blockchain and AI are two of the hottest topics in computer science, and it turns out they have quite a symbiotic relationship
Combination of Blockchain, AI Could Create Massive Synergies

Blockchain technology and artificial intelligence are two of the most interesting and disruptive technologies out there. While they are both extremely innovative, they have their own flaws. This is why people are now bringing these two amazing technologies together to create some interesting use cases. So, before we continue, let’s understand what these technologies are.

Blockchain technology

A Blockchain is, in the simplest of terms, a time-stamped series of immutable data records that are managed by a cluster of computers not owned by any single entity. Each of these blocks of data (i.e. block) are secured and bound to each other using cryptographic principles (i.e. chain).

The main advantage of this system is obvious. There is no central authority or single point of failure, and the system is created to be as democratic as possible.

Artificial intelligence

Artificial Intelligence is intelligence that is defined by machines. As Wikipedia puts it:

“AI research is defined as the study of "intelligent agents:" any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.”

The main advantage of AI is the sheer amount of flexibility it provides. In a normal system, you need to put in each and every line of code for a machine to act in a certain manner. However, via AI, you can enable the machine to react to any changes in its perceived environment.

So, now that we have defined what both these technologies are, let’s see how they can complement each other.

First, let’s look at how AI can help solve some of the Blockchain’s problems.

How AI helps Blockchain: Mining wastage

Proof of Work mining is incredibly wasteful. Miners spend $400 mln worth of electricity annually on mining. If that doesn’t put things into perspective, then imagine this: Bitcoin mining consumes more power than the entire country of Portugal! While the proof-of-work system is secure, the fact remains that it is power wastage for the sake of power wastage.

AI has already shown that it can be used to control the amount of energy wasted. DeepMind AI has already been deployed to reduce the energy wasted in Google Data Centers by a whopping 40 percent.

How AI helps Blockchain: Blockchain scalability

Everybody knows that the biggest problem that Blockchain technology is facing is scalability, or to be more accurate, the lack of it. Bitcoin manages only seven transactions per second while Ethereum does a little better at 20.

AI can help Blockchain take scalability to another level via features like Federated Learning. Federated Learning is what Google is using to make your smartphones smarter. It is a machine learning technique that allows your phone to learn directly via your input without having to send your data to the cloud.

Since it provides immediate improvement to performance and user experience, you end up saving a lot of time. This same method can be used in Blockchain technology to propagate data without spending a lot of time waiting for individual nodes to come to a consensus.

Now, let’s look at the other side of the equation. How can AI improve its functionality via Blockchain technology?

How Blockchain helps AI: Big data 

AI is extremely hungry for big data and needs a constant flow of it. Back in 2001, Microsoft researchers Banko and Brill did an interesting study. They found out that for an algorithm, the more data you feed it, the less error-prone it will be. In fact, the error rate will fall exponentially if enough data is used. This was further highlighted in 2007 when Google researchers Halevy, Norvig and Pereira published a paper titled “The Unreasonable Effectiveness of Data.”

Fine, so if you feed your AI more data, you will make it smarter. What’s the problem here?

The problem is in storage. The storage demands of AI are extremely impractical when it is in operation. It can eat all the data that it wants but that data needs to be stored somewhere. The architecture of the Blockchain itself solves this problem. The Blockchain is decentralized and encourages its participants to engage in secure data sharing. There doesn’t need to be a centralized entity to store the data anymore.

How Blockchain helps AI: Avoiding centralization

Speaking of centralization, since AI needs a lot of big data, a corporation can simply “own” an AI by feeding it their data. Connecting an AI with the Blockchain will make sure the data is fed from a decentralized entity.

How Blockchain helps AI: Data trails

The Blockchain is a completely transparent open ledger. Anyone can look at the data inside and anyone can trace that data to its very beginning. Having access to that level of data traceability puts a lot of accountability on the participants involved. This can help in two major ways:

  •      Byzantine/Malicious actors may want to sabotage the AI by feeding it useless data. Having a transparent system where anyone can trace the data all the way to its originator will make sure that people are discouraged from doing so.

 

  •      When the bots interact with each other, having a clear audit trail of all the data will help improve machine-to-machine interaction.

Blockchain + AI examples

We have already seen some exciting implementations of Blockchain and AI. One of the most interesting products of this communion is Augur. Augur is a trustless, open-source, decentralized oracle and prediction market platform built on the Ethereum Blockchain. The Augur AI uses the “wisdom of the crowd” or the “collective intelligence” of the masses to make accurate predictions.

Blockchain + AI: Symbiotic relationship

As we can see, the relationship between AI and Blockchain can be extremely intriguing. They seem to be capable of a truly symbiotic relationship with one entity making up for the other’s weaknesses. With platforms like Augur, we have already gotten a mere glimpse of what this collaboration is capable of. However, we have only just scratched the surface, more research definitely needs to be done. We could be on the cusp of something truly special here.

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One-to-One 3D Service: How Meituan’s AI Product “B-BOX” Serves Customers

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Chinese group buying website Meituan is going to launch their full scene AI product “Better-Box” to serve customers by the end of this month
One-to-One 3D Service: How Meituan’s AI Product “B-BOX” Serves Customers

Chinese group buying website Meituan is going to launch its first full scene AI product “Better-Box” to serve customers by the end of this month.

The new product combines AI, Cloud Big Data, machine vision and 3D photographic projection technologies to provide virtual characters to serve users. The name came from Meituan’s “eat better live better” business mission.  

What “B-BOX” looks like?

B-Box is shaped like a cube, with an AR optical see-through projector on the top. Also, there is a 10mm-AI-chip inside called M1000 to support its operation, apart from that we cannot see other holes outside the box.

Photo via Sohu

One-to-One Service: improve user’s shopping experience

Recently most merchants are paying more attention to consumer’s consumption experience to win profits. The launching of B-Box aims at helping customers make a better choice.

“If you open Meituan’s mobile app, you can talk to the 3D virtual character,” said Renjie Yu, Meituan’s AI technician. “B-Box uses Cloud Computing technology to analysis consumers’ shopping preference, that’s why you can talk to the virtual person in the real time.”

The product will be widely used in all entertainment places such as karaoke, restaurants, bars and hotels. Besides that, B-Box can also help vulnerable populations such as speech handicapped people to better communicate with Uber drivers.

Talking to a 3D virtual character, Photo via China Scitechnology Business
Talking to a 3D virtual character, Photo via China Scitechnology Business

“I am really surprised at their AR Magic Sensor system,” said Siyu Wang, a Meituan user who studies in Shanghai. “ I don’t like making choices; I prefer someone recommend dishes for me especially cater to my taste, that makes me special.”

B-Box not only provides customers better service but also helps merchants know their customers and find solutions to the existing consumer market. As one of the largest retail platforms in China, there are two million merchants registered on Meituan, the company said B-Box is the start of applying AI to customized service.

Now the product is going into the production process and will be put into use on the Meituan platform on April 31.

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Piglet’s Big Brother: How AI Changes Agriculture

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Agriculture, considered as something old-fashioned, is increasingly on the cutting edge of artificial intelligence
Piglet’s Big Brother: How AI Changes Agriculture

Piglet’s Big Brother

Throughout the history of time, scientists and farmers have been working together, searching for ways to advance agricultural practices by utilizing cutting-edge technology to increase yields and profit, while ensuring crops and animals are happy and healthy. Now, we’re seeing the use of deep learning and specifically computer vision emerging in agriculture, revolutionizing the industry that feeds billions.  

Computer vision for crop analysis: don’t just see, observe

Computer vision for crop analysis

Modern agriculture is a huge industry. Feeding billions of people takes a lot of land, and it becomes virtually impossible to process everything by hand. In particular, devastating plant diseases and pest invasions have often led to failed crops, and with the modern scale of agriculture, it is hard to spot such an invasion early on.

This is an important area where computer vision algorithms can help. Plant breeders use computer vision to recognize crop disease, both in detail from close-up views of leaves and plants and recognizing early onsets of plant disease and pests from surveillance photos.

All of this research is usually based on the modern go-to approach to computer vision: convolutional neural networks (CNNs).

Note that computer vision can be understood very broadly here. In many situations, photos are not the best kind of data that can shed light on important aspects of plant life. Often it is even better to use remote sensing data such as hyperspectral imaging or 3D scanning, which are increasingly used in agriscience.

This kind of data is usually high-dimensional and closer in nature to medical imaging than photos: check out, for example, a crop surveillance system called AgMRI. This requires special models for processing, but it still has the spatial structure required to apply modern computer vision techniques, in particular, convolutional neural networks.

This is, pardon the pun, a rapidly growing field: for example, recently $37 mln were invested into the establishment of Plant Phenotyping and Imaging Research Centre at the University of Saskatchewan.

The entire institution is devoted to exactly to the purpose of gathering big data about crops, usually in the form of photos and remote sensing data, linking extracted phenotype data to plant genotypes, to improve agriculture around the world.

Robotics in agriculture

Robotics in agriculture

Meet Prospero, the adorable farming robot that can’t help but remind you of WALL-E. It can drill a hole and plant something inside, accounting for local features of the terrain while following the predefined general patterns.

Then it can tend the crops, working with each one individually. And when the time comes, it can harvest, again being able to treat each plant in exactly the way it needs.

The idea behind Prospero is swarm farming: watch this video and imagine an army of small Prosperos crawling through the fields, leaving neat, perfect lines of crops behind.

Interestingly, Prospero predates much of the latest deep learning craze. It appeared back in 2011, but still remains a prototype that has not seen widespread use yet. But by now, robots are overcoming agriculture, automating more and more repetitive tasks:

  • automated flying drones are spraying the crops, and are  much more accurate with dangerous chemicals than full-sized airplanes; the same spraying drones can also be used to gather the aerial photos needed for computer vision applications we discussed in the first part

  • specialized harvesting robots are being increasingly developed and used; we have had grain harvesters for a long time, but only now, with the help of modern AI in both computer vision and robotics, we can develop a robot that can pick strawberries

  • Hortibot recently developed by researchers at Aarhus University, Denmark, can identify and eliminate weeds, either removing them mechanically or precision-spraying with herbicides

While many of these robots are still prototypes or being tested in small circulation, it is fair to say that robotics and agriculture are clearly made for each other, with more agricultural work to be automated in the nearest future.

Video tracking for farm animals: piglet’s big brother

Video tracking for farm animals: piglet’s big brother

There is a third agricultural application that’s worth discussing, a pilot project that the Neuromation company is planning to launch this year, with the potential to revolutionize an entire industry—and a large one, too. The plan is to bring modern computer vision to an industry that has not yet received much attention from the deep learning community: animal husbandry.

Naturally, people have already tried to bring big data and automated tracking to farm animals. For instance, a Pakistani startup called Cowlar produced a wearable collar that tracks activity and temperature of cows and buffaloes, under a catchy slogan of “FitBit for Cows,” and French researchers have been developing face recognition for cows. In this project, automated data collection and computer vision are used in a previously overlooked trillion-dollar industry—pig farming.

The bulk of the costs in pig farming falls on feeding the animals, bringing them from piglets to maturity as efficiently as possible. So optimizing the feeding process is what pig farming is mostly about.

Farmers would gain enormously from having detailed information about how the pigs are progressing. Currently, animals are usually weighed only twice over their whole lives: before and after feeding. It would not be too hard to tailor the feeding to individual pigs, significantly improving the output, if we knew how each piglet is doing with the feeding.

But this is where the twist lies.  It's easy enough to weigh a pig, although it causes stress for the animal and stress leads to weight loss! This catch-22 means that while there’d be data on a small subset of select pigs, it would not be worth it to weigh every pig as often as desired- or, really, at all.

This is a novel, non-invasive approach to weighing animals: a computer vision model that will accurately estimate the weight by photo and video data. The result will feed into analytic machine learning models that will improve the feeding process, leading to more sweet and tender pork for next year’s New Year celebrations.

Summary: agriculture on the cutting edge

Summary: agriculture on the cutting edge

It is often customary to stereotype agriculture and farming as something old-fashioned and outdated. However, as we have seen, agriculture is increasingly on the cutting edge of artificial intelligence.

The reason is that many agricultural problems are at the same time:

  • hard enough that they could not be automated before the advent of modern AI and deep learning: crops and pigs are alike, but not quite as alike as cars on a Henry Ford conveyor belt, so human work has been absolutely necessary up until very recently

  • easy enough that by now we already have the tools to tackle them, respecting the individual differences between plants and animals but at the same time learning to automate general techniques of handling them, driving a tractor on a field is easier than driving a car in human traffic, and measuring a pig’s weight easier than talking to people.

Agriculture is still one of the largest and most important industries on the planet, with even small increases in efficiency leading to huge windfalls due to the massive scale of things. For all these reasons the trend to automate agriculture is to continue into 2018 and beyond.

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