Jul 7 2019
What Language to Choose to Talk to AI
How developers choose the language for AI
The choice of a programming language when developing AI systems depends on whether it is a question of developing a model and its training, or writing a code for a target platform where a trained (inference) model will operate. Some aspects should be taken into account that relate to the need for additional training of the model during the operation or use of the Reinforcement Learning technology.
As a rule, target platforms use a number of restrictions; pre-compiled trained models for execution in the environment of low-level C-family languages are downloaded on them.
The relevance of a programming language in this context is determined by its ability to meet three basic requirements:
- Ease of complex models development and the availability of macroprogramming frameworks.
- Accessibility of open codes for study solving problems of AI implementation.
- Ability to port the developed model code to the required target platforms after training.
A large number of AI projects in the open access (for example, on Github), the presence of the well-established communities around Stack Overflow and other forums for Python programmers allow considering it as the language that complies with the requirement for accessible study.
Finally, such frameworks as Google Services and others provide an efficient migration of the developed Python programs to the target platforms, which greatly speeds up the transfer of the developed models into production. Thus, the requirement for code porting is met.
C ++ can be considered the second relevant language, in which many models are developed for execution in a distributed environment. However, it is inferior to Python due to the limited number of AI frameworks in this language.
Java can be called the third most relevant language for the implementation of AI models.
How languages improve as AI progresses
Today we should not associate the development of AI technologies with the improvement of programming languages.
To develop AI models, universal and convenient languages are applied, not specialized ones. It allows developers to keep up with a constantly updating variety of AI technologies with no need to study new tools.
Today there is a trend towards the complication of development. It is natural, since each new technology and AI cause the emergence of more complex models, the training of which requires sophisticated calculations over huge data sets that are combined into special structures, such as high-ranking tensors.
The task of new software tools is to minimize the growth of model complexity for developers. Tensors become objects with controlled functions that are called within the same programming language as previously.
Make it easier and cheaper
Today the development of AI projects can be considered complicated because there are not so many professionals on the market who understand the machine learning paradigm. Quite often companies face the challenge of finding developers and managers for AI-based projects.
Another aspect is the need to use specific hardware and software platforms during the development process. They are quite expensive even when renting in cloud services.
In this case, another problem arises, that is the search of qualified administrators and DevOps engineers. Overcoming the personnel gap between a narrow layer of highly skilled developers and researchers and developers of applied industrial AI is an important task. Solving this problem will allow transferring the development of AI projects from “complex” to “ordinary” ones.
The cost reduction of AI developments can occur with the enhancement of tools. The efficient direction here can be the enhancement of visual programming tools of the trained models. An example would be Microsoft Azure Machine Learning Studio. It is also necessary to take into account the active use of powerful AI for building other specialized AI according to specifications, as implemented in Cloud AutoML and Auto-Keras open framework.
The language of the future
With the development of AI tools, the focus will shift from programming languages to user interfaces for interacting with powerful designers of machine learning models.
However, since real projects, as a rule, are not purely trained machines, there will always be a need for the so-called “software glue” to combine technologically heterogeneous components into a single project. And the role of such a “glue” will remain behind a convenient programming language that will suit not only developers of AI components, but also other parts of the software project.
In future, frameworks for building learning models will be improved. They will gradually be included in the number of tools for visual programming and will be easily integrated with cloud-based AI.
It is likely that soon AI will be able to program itself. NASNet Google was the first example of creating a learning model for AI by another artificial intelligence. It was generated by Google AutoML in 2017. Since then, this AI has reproduced quite a few specialized “artificial children.”
Thanks AI TIME JOURNAL for sharing Artezio expertise :)
Jun 30 2019
How Technology can Change Business: from Startups to Innovations
IT as the main trend of our time
We live in the age of the rapid technology evolution, and this speed is merciless to those who stay in the old paradigm of business development. It relates to each industry and market and causes system changes inside of them. The more diverse and unstructured the market is, the more serious revolutions it will face in the next 5-10 years.
Automation is the core trend of our time. The main players strive for process automation in all the areas, from the service industry to education sector. Such platforms as YouDo and Skyeng which bring a client and service provider together are based on robotization. Thanks to AI and big data analysis, the platforms automate complicated intellectual processes. The platforms help structure the market and mitigate tensions between the client and service provider.
Perhaps more complicated expert issues will be solved in the same way very soon, for example, marketing or HR consulting. It’s safe to say that it’s already a reality, and there is some evidence. Prices are getting more transparent, a new class of specialists is being formed, and the result of launching new startups is becoming more predictable. This is the future which exists already today but is still sporadic.
Innovations and startups: what can change IT business?
According to the experts, it’s innovations that are the main reason for changes in the IT business. They let develop new fundamental solutions which are implemented in our daily life. Startups don’t really affect the IT business globally but with no doubt contribute to its development. As a rule, startups are created by specialists who actively use the existing IT solutions and enhance them. For example, Ark startup was built on the solution aiming to integrate the blockchain networks, which were previously not compatible. TrustToken developed an extremely flexible system that allows working with real assets, such as stocks, shares, patents, and real estate. All this influences the IT business, develops it, and opens new horizons for opportunities.
It is worth noting that usually large companies regulate the IT business. To achieve some changes, the communication between main players and startups should be set up. Startups need to know the restrictions in which large companies operate, while the market leaders should take into account the agility of startups. If this interaction is introduced, the speed of changes will be high.
The horizon of changes
A new ecosystem will be created around companies engaged in digitalization. It will be more attractive for investors, therefore, businesses should start thinking about it right now. Innovative technology solutions are likely to appear on the market, investments in already known technologies, such as IoT, will only increase.
Speaking of the main trends that influence business, first, IoT should be mentioned which is becoming necessary almost in any industry.
IoT solutions let businesses get valuable analytical information that is extremely necessary for the process of digitalization. Many similar technologies become smart, for instance, data storages based on software which is capable of searching and processing content in decentralized packages of structured and unstructured data for its further analyzing, formatting and indexing. For example, Hitachi Content Intelligence software allows extracting data and transferring it to working structures for further processing.
To implement such solutions, IT departments should interact more closely with operational business processes. It would let them focus on actual enterprise goals and determine the scope and direction of an IoT project together with top management.
The blockchain technology still has a great impact on business. It continues to evolve due to the active use of cryptocurrencies and blockchain implementation in the finance sector to solve industry-related issues. Blockchain-based solutions for internal processes regulation, such as KYC (Know Your Customer) and CIP (Customer Identification Program), working with client documentation, reporting, and much more, are going to be implemented in the finance sector this year.
Another big trend is digital transformation. It will lead to a higher speed of operation processing, reducing manual work, which requires increased costs. Companies will extract valuable information from big data and BI.
Machine learning and process automation contribute to the automation of routine operations. It will help move to digital economy to make sure that more time is spent on making decisions that are valuable to business instead of dealing with routine operations.
Are global changes coming soon?
Due to the gradual digitalization of processes, global economic changes are simply inevitable. But it takes time for the public to notice the changes and be happy with them. Innovations and startups are changing business incrementally, sometimes overcoming impediments and law restrictions. For example, blockchain could be more popular and world-spread if legislation systems of many countries were more flexible in terms of cutting-edge technologies.
The transition to digital technologies causes fears about data security. GDPR, a recent regulation in EU law that requires businesses to protect personal data, has become an impediment for blockchain development in Europe. Therefore, not all technological innovations can be applied in all areas. Business experts claim that an integrated system of legislation synchronization could be a way to implementing innovations into the business sector. For example, the introduction of Ministry of Technology could speed up the process of legislation adaption to fast modern technologies and tools. But there are still no such organizations in the world. Even developed countries face this problem when laws have to catch up with technologies.
Nevertheless, business is still developing. Already today, there is a transition to a model that encourages companies to develop their own program solutions and manage processes inside their company. If 20-25 years ago, it was popular to produce hardware, 10-15 – implement software, then now solutions grown within a company are getting more attention. Companies start investing in unique projects that have a world value. It lets them compete on the market not only today but also in decades when business will become fully digitalized.
Company information Artezio
Since establishment in 2000, Artezio has completed over 1000 projects for its international corporate customer base. The time and effort of the most experienced developers is also invested in R&D activities, including but not limited to AI, ML, and blockchain.
As a senior-level technology company we continuously strive to gain expertise and stay on top of the curve of innovation.
Our competences include:
- Enterprise application development
- Business process automation software based different BPM engines (Activiti BPM, jBPM)
- High loaded based SOA system development using both whole JEE stack (CDI, EJB, JPA, JSP, JSF, JMS, SOAP, REST) plus related frameworks(Spring, Seam) and whole .Net stack (ASP.Net MVC, WPF, WCF)
- Portal solutions based on Liferay Portal , Sharepoint WSS/MOSS, WebLogic Portal, WebSphere Portal
- Integrations solutions using ESB (OSB, Oracle ESB, IBM WebSphere ESB, JBoss ESB), Spring integration, MSMQ, eMule
- System based reporting Jasper, BIRT, Pentaho, SQL Server Reporting Services
- Large, distributed systems and highly-scalable network servers using GoLang
- Cloud based solutions (Azure, Amazon)
- Big data related technology
- Database analysis and BI (Pentaho, Oracle BI)
- NoSQL databases (Cassandra, MongoDB, CouchDB)
- Search engines (Elastic search, Apache Solr, Sphinx)
- Intellectual Data Processing (Apache Lucene, Apache Mahout)
- Distributed Compilation (Apache Hadoop, Apache ZooKeeper)
- Mobile and web development services.
At Artezio we do not believe in "one-fits-all" solutions, so individual approach, proven Agile-based customer collaboration frameworks, flexibility of the development process, and MVP delivery model ensure that Customers always get the product which meets market requirements.
|Type of company||Head Office|
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Key figures Artezio
Mr. Dmitry Rodionov
Deputy MD/Chief Operating Officer (COO)
- Service provider
Other classifications (for some countries)
NAICS (US 2012) :
Custom Computer Programming Services (541511)
SIC (US 1987) :
Computer Programming Services (7371)
What Language to Choose to Talk to AI