Artificial Intelligence is increasingly becoming an irreplaceable part of mobile applications, software and
CRM systems across different types. Payroll is a significant item in a company's budget, as it ranges from 20-30 to 60% of costs, making up 40% on average. Implementing AI algorithms in technological processes means abandoning low-skilled and mid-level employees, reducing labor costs and progressively increasing profits. Let's take a look at examples showing how the introduction and adaptation of AI into business process structure proves effectiveness and how much does it cost to develop an AI application.
GPT-4 Vision and Jupyter Notebook symbiosis
Recently released version 4 of GPT Vision interacts well with the interactive Jupyter Notebook. By augmenting the AI with code written in Python, the user, in response to a drawing made by hand movement, receives generated clear graphs in the form of parabolas or sinusoids, circular multicenter images. Entering a description with numbers and approximate curves produces a detailed visualization of a given format. Multimodality and relatively accurate visual estimation are used in situations when it is necessary to determine locations, analyze and interpret pictures “on the sheet”, calculate the model according to specified mathematical parameters.
This solution is useful for engineers and designers, builders and analysts. Just sketch a drawing by hand and supplement it with textual clarifications, and the software will produce a ready-made chart, diagram or plan with clear lines and dimensions in a matter of seconds. Simple code written in Python and embedded in AI services provides a clear reproduction of a given linear format. In order to solve complex problems, import modules and packages, download and compile distributions, and install other libraries.
Jupyter project has such options for implementing and developing AI algorithms: web environment, application for calculating analytics and digital data, simplified version of static pages, widgets and dashboards with multilateral interaction. One or more elements of the functionality can be used depending on what the developers' task is, adapting the AI to the requests.
Labelme application and Deepface libraries
Visual annotation is a new step in AI programming and implementation. Python's open source code and simplified programming form provides visual annotation followed by processing into a rigorous logical structure. Computer vision is a segment of AI. It recognizes and processes visual information, makes analysis of video, content and other images using data from a previously generated database.
Labelme is an example of a classic graphical application created on the open-source LabelMe platform developed by Massachusetts-based specialists in 2008. Segmentation and classification, customizable UI-format guarantee convenient manual markup work online or offline. Qt is the graphical tool of its interface.
Face recognition in real time using the Deepface library, created in Python, provides human identification with an accuracy of 98-99%. Likewise, AI models based on it identify a person's age and emotions, gender, instantly comparing with hundreds of images. The library is the extract of tested model solutions like VGG-Face and OpenFace, ArcFace with Dlib, GhostFaceNet and others, performing recognition tasks as a detector within 5 seconds. This is important for security in places with high concentration and passability of people - airports, stations, shopping centers.
Implementation of AI modules and comparison based on Mistral 7B
The GPT sector has reached a point where LLM neural models are working on half of the tasks in the business. Implementing AI-features and AI Tool in BPM extends the lifecycle by simplifying identification and initial analysis. AI Tool also performs redesign with new solution implementation and follow-up monitoring. Benchmark embedded AI modules for business cases include:
- Evaluating database and data operations;
- CRM for catalogs and marketplace automation;
- integration with other APIs and plugins;
- fulfillment of marketing tasks (as an assistant to a marketer);
- evaluation of action logic and code success.
Such solutions are more often developed on closed code to ensure the security of users and owners. Testing and writing prompts is the basis, the “heart” of the idea. The Mistral 7B model in the Apache 2.0 public license is among the best available today. The right model can be selected, developed and launched only by an experienced team of specialists who will assess the scope of tasks, availability of resources and facilities, and the utilized programming language.
ML: an example of model-based language learning
ML machine learning is characterized by many directions - conventional protocols and deep reinforcement learning, language models, matrix or tensor methods. ML applications are in industry and knowledge-intensive technologies, environmental sciences, neurobiology and climatology, for improving robot actions and autonomous personal transportation. For example, a language model will
predict 4-8 subsequent tokens more accurately after training on global patterns, showing better performance compared to training on local protocols.
Multitask decoding is based on the interaction of multiple target variables and detected regression relationships, data evaluation on a loss scale and their subsequent balancing to achieve the desired effect. These are high-level tasks, so the cost of developing such cross-platform AI applications starts from $100-150 thousand. The training duration of several models on a complex multifactor architecture with the introduction of algorithmic reasoning ranges from 300 to 500 thousand hours, which explains the high cost of such projects.
Expanding the range of language model usage is clearly illustrated by Gemma (a Gemini API product), which runs in 2B and 7B tests. The Keras 3.0 library embedded in the model is responsible for compatibility with JAX and PyTorch frameworks, open training library TensorFlow, preserving high operation performance and flexibility of the proposed solutions. Extending the existing functionality to meet business requirements is supported by interpolation of variables, interpreter parameter customization, unit testing and debugging with profiling.
Wegic builds and publishes websites
Successful implementation of AI can be clearly seen on the example of the Wegic platform, which replaces programmer and UI/UX designer in one person. It is sufficient to write a competent prompt, make clarifications, specify the desired color scheme and location of elements, so that the site is ready without writing code. It is not possible to make complex versions of sites with hundreds of pages and categories, with deployable menus and a marketplace that supports thousands of transactions with secure financial gateways.
However, the technical and software capabilities of the platform are enough to generate a business card site, a personal brand page with a portfolio, a simple
online shoe store with a small number of positions. Such a platform can also be developed for other purposes - for example, for AI-modeling of the building and house interiors, road design, life support complexes and food production. The first three sites on Wegic are free - 120 credits are given. When they run out, payment for low-cost plans starts at $10 per month. Once a site is created, the platform publishes it online after a short time.
An iPhone with built-in OpenAI and “personal memory” AI
In early 2024, there will be more than 1.5 billion iPhone owners in the world. About every fifth inhabitant in the world is Apple's mobile user. To improve the work of Siri, it became known that ChatGPT will be implemented in the 18th version of iOS. The OpenAI owners have not fully agreed on the terms yet, but the fact that the chatbot Gemini will be an element of the update, indicates readiness for the next technological shift in the field of AI. The details of this will be revealed in June 2024.
The given information confirms the fact that the solutions of OpenAI and other developments in the field of neural networks are gaining momentum. Immediate analysis of customer data, segmentation of requests and financial assets, maintaining personal contact based on previous transactions - a small list of AI capabilities that are worth implementing in a business project to increase profitability.
Database will remember that a specific person ordered an unmanned cab with a child seat. The next time the application with built-in AI will specify whether a car seat is needed. A laptop buyer in a year or two will be offered to upgrade to a new model that is better and more powerful than the previous version. The AI will send a favorable offer with a basket of the preferred set if it is known that during certain periods of the year people buy only fish and seafood, refusing meat, eggs and milk.
Neural networks need clear protocols
As a result, Pennsylvanians have developed the DrEureka platform, where AI language models teach robots. Using the example of a robot dog, the AI showed how to generate code and reward or punish the robot step by step as a result of each successfully executed simulation, taking into account balancing based on the machine's mass and displacement in space. The special feature is the creation and execution of several scenarios simultaneously, which is considered possible only for humans.
Here is an example: a woman can talk on the phone with a wireless device, fry steaks and pour yogurt for her child at the same time, switching then to other matters. Nowadays, a neural network generates and executes up to a dozen algorithms of actions in parallel. But we need control and well-defined prohibition protocols, because in order to achieve above-threshold efficiency and energy saving, the AI might allow dangerous actions.
Presumably, to calculate that a drone car will travel faster on three wheels. It may be considered that unfamiliar relatives who came to visit during the absence of the owners are burglars, so it is necessary to block windows and doors and call the guard. For this purpose, you need a control with the indication of absolute prohibition to perform certain operations.
ZeST as a foundation for graphic design
Low-cost applications priced in the range of $20-50 thousand can be based on ZeST-type methods, where a pilot sample changes its appearance and texture based on the material. Despite the 2D format, a fixed reference point on the properties of the donor material completely transfers nuances to the original object, adjusting scale and illumination. Depth and color shades are encoded by IP-Adaptor while preserving other object visual differences. The method is partially similar to B-LoRA and styling principles in InstantStyle.
AI-modified texture will be useful for furniture and fabric manufacturers, porcelain manufacturers, and other manufacturers where it is necessary to adjust the color scheme. The method embodies an indispensable “magic wand” for graphic design, exterior and interior building design. Suppose the customer wants to finish the living room and bedroom in the style of rococo or baroque, classicism or luxury. Choose the right elements and the AI application generates them anew in the right palette, instantly presenting a number of prototypes.
DALL-E, VQGAN and CLIP for multimodal generation
Multimodal creativity is recognized as a tool for psychotherapy, a way to improve spatial thinking and to develop multi-picture projects. The
tokenization in DALL-E is such that half of the picture is formed based on the drawing, the other half by text. Neural networks, once trained, often generate a viral image taking into account spatial parameters, events and emotions,
make NFT tokens. Netflix, website generator Jekyll and search site Yelp, social networks Facebook and Twitter use its resources to increase the number of target audience.
These functions are also suitable for creating games, design, visual support of the project, so they can be considered a component of society with increasing importance. The analog of the closed DALL-E is CLIP, the functionality of which has been cut by half in a comparison with the original. An extension of the two neural networks is the adversarial VQGAN, which works in an adversarial generation format where the generator and discriminator compete. VQGAN and CLIP interact perfectly, as the former generates the image and the latter as a ranker analyzes the relevance to the task.
The greatest cost of training neural networks is in data collection and subsequent
AI development. In order to produce high resolution pictures, the quantized encoder and decoder are taught to reconstruct patterns based on semantics. It requires a codebook and vector quantization with distribution. A problem exists in the limited volume of convolutional layers and transformer architecture considering quadratic scalability. That is why moving away from pixels to code words with index sequences, using Colab service is a way out of the resource scarcity problem.
AI assistant Verba and Trillium are training AI models
Verba applications are universal AI-assistants. It works with local data and cloud resources, responds to queries, retrieves necessary information, and generates reports. The application operates using the RAG method, leverages the Weaviate vector database and their repository. The software interacts with LLMs such as HuggingFace or Ollama language model, OpenAI framework, Cohere platform.
Trillium, the 6th generation TPU that Google will be releasing on a mass scale soon, combined with optical switches, stands ready to train AI models of low to medium complexity. Trillium is 5 times faster than the previous version, contains 256 working chips in a single unit. TPU is capable of utilizing 4096 chips in a Multislice-functioning cluster. There are hundreds of “pods” in the cluster itself.
If we take into account that the average annual salary of an employee in the US and developed EU countries is $50-60 thousand, and the development of an AI application will replace one to three to five people, the economic benefits are obvious. Neuron model training, creation and implementation of AI-application in CRM of medium complexity level will pay for itself in 3-12 months. Development of the data feed structure, algorithms for engaging updated modules and analyzing relational bases require the usage of parallel programming and sockets, testing operations during the launch process, so the order price may be higher.
Self-Discover solves problems with the method of self-discovery
That language models are constantly improving their own functionality is clearly demonstrated by the novelty of Self-Discover with the kernel, where the LLM selects atomic reasoning modules with critical and step-by-step operations during decoding in a self-discovery process. It is better than a daisy-chain way of thinking, as each step is followed by inference specific to the human way of thinking with a reasoning program, meta- and direct cues.
Self-Discover is based on the principle of self-consistency and paradoxical reasoning, when an AI model creates a logically correct algorithm on the basis of the involved stack. Universal reasoning goes through the stages of selecting a way to solve the problem, adapting it to specific conditions and direct execution. The environment is suitable for solving complex tests, reasoning structures are implemented and transferred to different LLMs.