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Pallium is a decentralized platform that allows for easy development, training, and storage of AI models. We're making AI available to everyone regardless of your technical background.

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It seems that the space agencies of the world will soon figure out how artificial intelligence is useful in space.

At the end of June, a robot was sent to the International space station. This robot is a new joint development of IBM and Airbus aircraft manufacturer. IBM was responsible for the "brain" of robot, and Airbus - for the creation of a corpus equipped with a mechanism for flight.

CIMON is a ball-shaped robot the size of a basketball. It was designed to help astronauts, it is autonomous and moves freely in zero gravity with help of air jets. German engineer Mattias Biniok developed a "brain" of robot on the Watson AI platform. Artificial intelligence of SIMON is based on a technique called supervised learning.

The robot can interact with anyone, but now CIMON has a specific mentor - astronaut Alexander Gerst, conducting experiments with the robot.

Whether a robot capable of learning will be involved in space missions to Mars depends on the success of the project.

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #NewTech #technology #neuralnetworks #robot #space
One of the latest developments in artificial intelligence allows to recognize the poses and movements of people... through the walls! The development based on radio waves – human bodies do not pass them. The artificial intelligence involved in the technology was trained to recognize human movements by analyzing a variety of photos. Next step was the formation of skeletal figures.

#Pallium #Palliumnetwork #AI #ML #neuralnetworks
A gastronomic app based on artificial intelligence that analyzes your personal taste preferences? In the 21st century it has become a reality!
What is capable of such an application? And, more importantly, will be the app drive modern food manufacturers to create so-called personalized food?
After all, is food fully adapted to our tastes, our metabolism and our sensitivity to allergens a myth or a future reality?..

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #neuralnetworks #food #app
Pallium Network is a distributed computing network for ML. It has processes and events occurring between its participants and getting managed also by them with the help of blockchain technology.

Pallium Network performs its mission by creating a distributed computing network with the internal economy. Pallium Network’s economy is based on the market of computation and information resources and entities’ (agents’) competences capable of performing an assigned task. Market participants regulate interactions between each other by themselves and transfer resources and competences directly to each other.

Practically it allows optimizing expenses of AI developers who provide competences and of those who use AI for business processes; it also makes it possible to gain profit for data owners and owners of computation powers and competences.

#Pallium #Palliumnetwork #Palliumnews #artificialintelligence #AI #machinelearning #ML #NewTech #economics #neuralnetworks #AIA
Uber has taken care of the ability to determine if a customer who wants get a cab using the app of this international service is drunk. The new development founded on artificial intelligence. The algorithm will have to analyze the extensive set of data to protect the service driver from a risky trip or redirect the client to a more prepared driver!

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #NewTech #innovations #neuralnetworks #apps #cab
Staff recruitment is an area that is based on human interaction. Anyway, one of the main components of the recruiter's work is constant communication with people.

But it turns out that artificial intelligence is already being introduced in this area quite significantly. AI scans the network in search of candidates, consider applications from applicants and even analyzes the staff turnover. This way AI frees HR time for other tasks.

How to increase your chances of getting a dream job, knowing that now you have to interact not only with people but also with their assistant – artificial intelligence?

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #NewTech #neuralnetworks #recruiting
We are open for cooperation with AI teams and developers. If your project needs additional computation resources or you are interested in testing our network or launching a common pilot project, feel free to contact us:

#Pallium #Palliumnetwork #Palliumnews #artificialintelligence #AI #machinelearning #ML #neuralnetworks #AIA
Artificial intelligence is actively introduced into casual life. Not everyone can keep track of the speed of innovation. It is even more difficult to keep up with innovations. However, entrepreneurs who want to constantly stay ahead of competitors should adopt the most current trends in the AI sphere, adapting them to the needs of their business.

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #NewTech #innovations #neuralnetworks
Some time ago we had a tricky interview with developers working on machine learning issues.
We were wondering which libraries, learning resources and frameworks the developers use, what problems they face with while training models: infrastructure issues related to computation capacities and to implementation of the final product.
Over 80 specialists from different countries took part in the study.

The first question was related to selection of libraries and frameworks for machine learning. 75% respondents use TensorFlow/Keras, 54% use Scikit-learn, and the third place is taken by Pandas as 34% respondents specify it, while NumPy and Pytorch are sharing the fourth and the fifth places with 29 and 26 % correspondingly. As for SciPy, Matplotlib, XGBoost, they take from 10 to 14%, when Seaborn and Theano have less than 10%. Apart from that, a few dozens of less popular resources and frameworks were mentioned, such as CatBoost, Matlab, Deeplearning4j, LightGBM, LIBSVM, H2O, spaCy, Gensim, Caffe and other.

Users explained why they choose TensorFlow/Keras: they are the most widespread, popular and even fancy instruments with a good community support, with huge and open documentation, with a handy and practical set of tools.

Besides, they are considered as powerful, flexible, able to be adapted to certain tasks, having multiple functions and implemented everything ‘needed for ML’. Almost the same way the respondents describe benefits of Scikit-learn and Pytorch, specifying they are well-developed and the best community-supported resources on Python.

As for private comments, the users who prefer Pytorch as the fastest and the lightest to TensorFlow, also praise Seaborn for user-friendly visualization tools.

#Pallium #Palliumnetwork #Palliumnews #artificialintelligence #AI #machinelearning #ML #NewTech #TensorFlow #Keras
We continue to share with you the results of the problem interview that we recently conducted for developers dealing with ML problems!

While describing benefits of libraries, the majority of respondents have much in common in comments, however when they answered the question about problems the developers face with when using chosen resources and frameworks, we got a full bunch of different notes and complaints.

This indicates not all tasks might be resolved on the current stacks, so the main part of coding has to be done manually; rather acceptable architecture solutions for a final product are not available; there are troubles with learning selections; the data quality is rather low; the resources are overused unreasonably; some people even openly complain about unavailability of cloud technologies and the bound need of working on powerful laptops.

Some users face with the problem of data import and processing, other point out conflicts with operation systems, for example Windows, while installing resources. There are people who sarcastically say that a majority of problems they have are related to a human mistake and not to technical issues.

However, with all that variety of problems, there are a few which are mentioned by almost all respondents: they include huge time needed to solve big tasks, the cost and volume of computation resources and issues with implementation of the final product.

#Pallium #Palliumnetwork #Palliumnews #artificialintelligence #AI #machinelearning #ML #neuralnetworks #libraries
When planning interview with developers, we expected TensorFlow to be the most popular library. And our expectations have been fully proved. However, we were wondering not only why that particular library is so popular – we told about it above referring to the answers of our vis-à-vis, but also why developers make their choice against our rating leader.

We found the following answers appeared to be the most understandable and distinct: it’s not good to perform development on C++; Pytorch is more advanced (without clear reasons) and productive. There was a certain pool of answers (about 20%) the authors of which consider TensorFlow as a near-term prospect and plan to use it in the future.

However, our interest lay not only in troubles with certain libraries but in common infrastructure problems which developers face with or don’t, while training their LM models. 11 lucky guys said they don’t have any issues. Three people said they have issues very rarely. The rest of respondents did have problems. However, they were usual and obvious related to libraries faults, compatibility problems, transferring models from one library to another, implementing models in production stage.

The biggest and most frequent problem is certainly computation capacities. As a result of this, a lot of time is spent for training models, especially at the stage when models get more complicated and data get more volume.
The problem of resources and computation capacities is obvious and essential. Where do our respondents take computation capacities and are they enough to solve tasks of different scale and complexity?

More than a half of them – over 51% use own capacities, PC or laptops, both own and even friends’ laptops. 26% refer to local servers and 17% use AWS and other cloud servers. About 5% possess capacities provided by institutes or companies where our vis-à-vis work in. There are few exceptional people who get capacities from God.

We thought how great it would be if anyone could get it because the last question of our interview was concerning availability or unavailability of computation capacities. And only 24% say they don’t have such problems either now or in the near-term prospect. 30% just the opposite, do feel lack of computation capacities and 36% are alright now but they see it as a problem in the future. And only a part of 36% is thinking about possible solutions: leasing additional capacities, referring to open resources.

There are people who are not willing for such solutions and would prefer to spend more time for training models. And there are other ones who dream to pay for additional capacities only when they are really working but not when other learning issues, for instance, concerning architecture, are getting solved.

#Pallium #Palliumnetwork #Palliumnews #artificialintelligence #AI #machinelearning #ML #TensorFlow #neuralnetworks
What is Pallium Network describing vividly, simply and finely.

#Pallium #Palliumnetwork #Palliumnews #artificialintelligence #AI #machinelearning #ML #AIA #IoT #BigData
Artificial intelligence transforms the sphere of design! AI comes to the aid of designers and builders, machine learning is integrated into design tools.
Print pedestrian bridge "in the air"? Planty of design options at your request? IoT in the service of developers?
Looks like the future belongs to generative design.

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #NewTech #generativedesign #IoT
The modern world challenges business every day - have to change or to die! These are realities: in many industries supply exceeds demand, and competition is high.

But how to change situation? What will be a convenient, affordable and effective tool for real change?

There is a solution that has already allowed a number of companies not just to survive, but to confidently take a leading position in their industry. This solution is artificial intelligence (AI).

How can AI help in business?
For example, generative design has enabled Airbus to reduce annual carbon dioxide emissions by half a million tons. Analytics based on Big data allowed banks to increase customer response to their offers by 400%. Generative adversarial networks of medical laboratories were able to increase the accuracy of recognition of rare pathologies by 20% and common diseases - by 40%.

Only for the first half of 2018 six leading startups in sphere of AI have collected to 5.9 billion dollars.

What problems hinder the development of the AI market?

More on that later!

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #business #neuralnetworks
We continue to talk about the role of artificial intelligence in modern business!
What problems hinder the dynamic development of AI market?

The first is the shortage of computing powers.
Our problem research, which we recently conducted among developers, showed that 76% of developers experience a lack of computing powers. At the same time, other studies show that the need for computing powers every 3.5 months increases by 2 times, and in the future 70% of developers will not have gain from the training of ML-models.

The second problem is the shortage of competent professionals! Analysts Forbes investigated the problem of AI in business and in society. They drew attention to the shortage of professionals competent in the sphere of AI. Therefore, not every organization can find and hire the right employee. In addition, when you hire an employee, you must create an appropriate work environment for the employee to obtain an AI-based product and implement it in your production processes.

What solution will be effective in this situation?
More on this later…

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #business #neuralnetworks
To sum up: artificial intelligence allows companies to develop more efficiently and helps to strengthen competitiveness. Artificial intelligence is an innovative tool. It is able to change business processes.

But the shortage of computing power hinders the development of the AI market. Shortage of staff also creates problems. There are not enough qualified specialists in AI in the labor market.

How does Pallium Network solve the key problems of the AI market?

To solve the problem of computing powers Pallium Network team uses private computing resources of users from around the world, whose participation in the training of ML-models is carried out for a remuneration.

Pallium Network creates a decentralized market where developers can lay out their products, and business can to look for and find the necessary AI-solutions. Becoming a member of the Pallium Network market, the business gets rid of the need to invest huge amounts of money in hiring, training and the maintaining of specialists, as well as in research and training of ML-models and simply finds suitable ready-made AI-solutions, buying them as a service.

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #business #neuralnetworks
Machine learning contributes to the emergence of innovative technologies in various spheres of life.
For example, scientists used ML, designing a new bionic prosthetic arm. And this prosthesis is capable of self-learning.
According to the researchers, self-learning bioelectric hand can mark the beginning of the next era of prosthetic limbs.

#Pallium #Palliumnetwork #artificialintelligence #AI #machinelearning #ML #neuralnetworks #bionichand