Understanding the different types of AI

AI technologies can be divided into AI hardware, software, and ultimately AI as a Service (AIaaS).

AI software
The different AI software that is currently being developed can be grouped into 4 segments: AI platforms, chatbots, deep learning software, and Machine learning software. AI platforms are needed to develop applications from scratch, including built-in algorithms. Chatbots are used for customer service. Nowadays online one first chats with a chatbot who answers recurring questions, and only when the bot is unable to answer the question the client is referred to an employee. This saves labor wages for the employee and makes processes more efficient.
Deep learning software includes speech recognition, instant translation services, or image recognition. Deep learning software, just as Chatbots, is already integrated with our daily lives like Alexa by Amazon or Siri by Apple.
Machine learning software enables the sifting through knowledge and data to best determine suitable practices or solutions.
For example, Deepbrain AI services are developing software such as video and speech synthesis and live chatbots. AI Humans are already at enterprises’ disposal and DeepBrain is continuously working to expand technology for everyone. DeepBrain has developed humans that can communicate and emphasize just like real people. The company contains three main parts: high-performance computing network, blockchain mainnet, and recently it has also launched AI hardware: a GPU computing mainnet. Furthermore, recently DeepBrain has just raised $44M capital to further expand its operations globally.

AI hardware
AI hardware, as it is used today, mainly consists out of 1. Central Processing Unit (CPU) which enables the design and programming of the chip 2. Graphic Processing Unit (GPU) is the chip that allows the display of the CPU 3. Field Programmable Gate Array (FPGA) is a reprogrammable computer chip, especially needed when new AI models need to be programmed due to a change in workload or 4. Application-Specific Integrated Circuits (ASIC) is typically developed for specialized cases where standard chips do not meet the requirements and tailored chips can do the job more efficiently.
According to studies, the demand for AI application-specific hardware will increase by 10–15% resulting in a $109bn AI hardware market by 2025.
Apple for example has been developing its hardware chips for a while which is powering their new products, as before they made use of Intel chips.

AI services
AI services refer to the outsourcing of AI solutions or tools. AIaaS are ready-made and easily to-be integrated solutions for enterprises to provide AI for applications and workflows. These are for example the robots in warehouses that work to sort and package parcels.

Some examples from companies that are developing AI technology:
Fetch AI is a Cambridge-based AI lab that builds tools and infrastructure to enable decentralized, and tokenized learning networks, and a digital economy. As it is open access, any user can connect to the world-scale dataset, which is able to carry out complex coordination tasks. Fetch AI provides a series of software agents that act on behalf of their owners. This is forecasted to optimize trading, reconfigure public transport networks, or connect energy networks in a smart grid.
In order to further facilitate and make communication more efficient, Botchain designed a ledger of bots that are communicating like their owners and their capabilities. Its aim is to become a standard way of sharing data between bots on the blockchain in a secure way.
A utility token has been released by AI Coin, with which holders get certain benefits based on the AI trading model and other technologies established by the founding team.
Matrix AI currently operates two major components: an AI-training platform and an AI-enhanced public blockchain. Combined, they are working on improving AI technology.
Numerai’s goal is to predict the behavior of the stock market. On their platform, one can build learning models based on abstract financial data to forecast the behavior of stocks. Those models can be staked, and rewards will be paid out based on performance.

A wide range of blue-chip companies are heavily investing in AI and integrating it as a key part of research and development. Tesla for example has announced AI as a key part of product development. Many companies are realizing that ultimately infinite scaling can solely be done with integrating AI. Microsoft invested around 1B the previous year into AI to create machines that can think more like humans. The revenue of its cloud service Azure has surged close to 60% for the first quarter. The frontrunner in terms of investment into AI investments is Google, however, which also clearly showed in its increasing revenue in the cloud computing services.

Solidus AI is building the infrastructure for AI/blockchain-based companies as one of its components is building data centers and providing computation-intensive data analysis tasks.

About Solidus AI Tech
Solidus AI Tech is a tech company that is bridging the gap of insufficient High-Performance Computing resources in Europe by building data centers throughout Europe. One facility in Budapest has already been built and the company is commencing to build an HPC infrastructure.
Solidus AI Tech will build the technology that will enable communities to feel more connected and safe and henceforth become the leader of the democratization of AI.