On-premises and private cloud
Having AI in your environment, be it on-prem or private cloud, can be up to 60% more cost-efficient than proprietary solutions. Purchasing the hardware and maintenance costs are also factored in.
What Is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a computer science field that develops software with human-like intelligence. These AI systems can understand any data to solve complex human problems. For example, it can read PDFs, text documents, and statistical numbers to perform various tasks with information received.
What Is AI?
AI stands for artificial intelligence, which is computer systems with human-like intelligence. It is a computer science field that has given computers the ability to understand human-generated data, learn its patterns, and take actions or provide insights for humans to complete goals. These include translating and generating text, analyzing images and videos, and comparing history data with live data. It can then process information found and produce output for businesses in various fields and industries.
The growth in artificial intelligence technology adoption has enabled businesses to understand information to make better business decisions, boost customer experience, and automate complex problems.
The growth in artificial intelligence technology adoption has enabled businesses to understand information to make better business decisions, boost customer experience, and automate complex problems.
History of AI
Early Foundations (1940s–1950s)
Let us take you back to the 40s and 50s when smart computer science guys like Alan Turing and John von Neumann began to think if machines can do what humans do. Alan Turing wrote an article, "Computing Machinery and Intelligence," introducing the Turing test. The idea behind it was simple - if a machine can act as a human, it has human intelligence. Meanwhile, John von Neumann developed a stored program known as the von Neumann architecture. It is the foundation of computers that allows them to store instructions and process data systematically. Together, they created these simple ideas that began the evolution of artificial intelligence as a scientific discipline.
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The Rise of AI Research (1950s–1970s)
Fast forward to the 50s and 70s, AI rose with the introduction of Artificial intelligence by John McCarthy at the real Dartmouth Conference. Scientists from all over the world were intrigued and thought AI would soon be as intelligent as humans. However, the technology could only solve general problems or logic, such as puzzles and small tasks. As a result, it was a time called Symbolic AI - classical artificial intelligence or logic-based artificial intelligence.
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AI Winter (1980s–1996)
But then, the problems started around the 80s and 90s because AI was not growing past lab tests. In short, AI hit a roadblock. With the loss of interest, money also stopped. It was the AI Winter period. Theories were there, but technology breakthroughs were not happening. The symbolic AI was not as powerful as people had hoped.
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Modern Resurgence (1997-2010)
In 1997, there was a resurgence of AI when IBM made Deep Blue-based machine learning and neural networks. As a result, AI got much better. It was even able to beat Kasparov in chess. Another great example was search engines. AI helped consolidate data and make it more available for humans and businesses. But these tools were nothing like the AI we know of today. They were still simple tools able to sort data and provide it in a more accessible format.
Key Breakthroughs (2010–Present)
Since 2010, AI received the required boost with deep learning and NLP advancements. That said, we can say AI finally broke through in around 2017. The development of Transformers by Google gave the needed boost for the foundation for AI. It changed everything. It was now able to understand human language. Not long after that, OpenAI developed the first large language model (LLM) known as GPT 1. It was followed by GPT-2 in 2019 and GPT-3 in 2020. What started as a boost needed to lift off AI has completely disrupted industries and markets. And it keeps growing at a faster rate than any other technology.
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How Does AI Work?
Artificial intelligence
Artificial intelligence (AI) is the overall term for making machines human-like. Examples are machines recognizing faces, delivering packages, or driving without human interaction. AI has many sub-fields, such as machine learning, deep learning, and generative AI. Each can perform different tasks. For example, generative AI can understand human language, something others cannot.
Machine learning
Machine learning (ML) is a subset of artificial intelligence that makes machines learn patterns from data and use them to make predictions. These systems do not follow rules set by strict programming. They use algorithms to improve their outcome from experiences and patterns. There are different ways they can do that. For example, you have supervised learning, where you train models with labeled data. Unsupervised learning is where the machine finds patterns by itself. Lastly, there is reinforcement learning, where the machine learns by making certain decisions and getting feedback, which it uses to provide better outcomes.
Deep learning
Deep learning is a subset of ML and uses artificial neural networks to learn from data. These neutrals are similar to human brain structures, which enable them to learn super complex patterns. It is not step-by-step programming either; the system learns by itself. As a result, it can recognize pictures, speech, or natural language.
Generative AI
Generative AI is the newest field of artificial intelligence and a subset of deep learning that uses many other AI field features. For example, it includes:
- Large Language models, which are part of machine learning
- Generative Adversarial Networks (GANs) from deep learning
- Tokenization and sequence modeling that are NLP methods
As a result, generative AI has the highest capabilities for creating realistic content such as images, text, and videos.
How Is AI Used Across Industries, and What Role Do Employees Play?
AI in Healthcare: Diagnostics like image recognition, drug discovery, and virtual assistants use a lot of AI, but doctors talk with the patient and provide procedures.
AI in Finance: Fraud detection, algorithmic trading, and credit scoring all use AI but still need human oversight, especially when making big decisions.
AI in Transportation: Autonomous vehicles and route optimizations work with AI, but humans should always be there to check and correct anything or intervene if something happens.
AI in Manufacturing and Supply Chain: Predictive maintenance, robots, and optimizing supply chains run with AI, but the human decision is required for the strategy and ensuring the system works.
AI in Retail: Personalized recommendations, inventory management, and automated customer service have incorporated AI. However, marketers and designers are required to come up with ideas and put the plans together.
AI in Entertainment: Content recommendations, video game NPCs, AI-assisted music/art creation, but artists shape the story, design, and characters.
AI in Agriculture: Precision farming, crop monitoring, and managing resources often use AI to optimize processes, but farmers still make overall strategy and main decisions on the field.
AI in Education: Personalized learning, auto-grading, and tutoring systems use AI for work, but teachers are still essential for making lessons, supporting students, and complex teaching needs.
Different Types of AI
AI has many types of stages, such as
- Narrow AI
- Self-aware AI
- Reactive machine AI
- Artificial superintelligence (ASI)
- Artificial general intelligence (AGI)
We are currently at stage 1, narrow AI and we need a breakthrough before we reach the second stage, self-aware AI.
What Are the Common Challenges of AI?
Data protection
Protecting sensitive data, such as internal company secrets and client's personal data, while using AI is a challenge. Many solutions are cloud-based, meaning you must move your data to their servers to use their services. As a result, you have no control over how and where they process data. Your data is vulnerable.
Development cost
Developing the underlying infrastructure, data connections, and enterprise security features is complex and costly. The reason for this is due to the high demand for AI engineers. Their salaries can be up to 1$ million a year, according to Tesla. This has resulted in large overheads for companies hiring AI teams to develop these internally.
High cloud overheads
There has been a massive transition into the cloud in the past decade. While this adoption still grows, many businesses realize that this is not feasible in the long term. This is true, especially with AI because it requires a large amount of processing power, which is too expensive when using cloud solutions.
How to Overcome AI Challenges?
Choose open-source-based AI technologies
These allow you to host systems in your environment. So you can have full control over technology, the backend and the front. This is the only way to ensure that data never leaves your premises and that you comply with regulations such as the EU AI ACT and GDPR.
Leverage ready-made solutions
Many ready-made solutions, such as advanced AI APIs, enable you to adopt AI fast. So, you can bring AI features to the market quickly, almost instantly.
Partner with an AI Company
When you have an AI company as a partner, you can rely on their expertise and stop spending time and funds building the underlying infrastructure from scratch. (Link to the homepage)
Benefits of AI For Businesses
Automate manual tasks
Manual tasks consume employees' time and hinder company growth. AI systems can automate various human resourceful tasks, allowing employees to work on tasks that are more important for success. For example, employees can cut time spent on writing summaries of meetings, comparing long insurance documents to provide the best quotes, and much more.
Make sense of data
Organizations have ever increasing flow of data, but using it for growth has been difficult. However, artificial intelligence introduces a new layer of making sense of data through machines able to interpret information for insights, generate text, or make data-based decisions. For example, AI can compare historical data with live data to find frauds in bank payments or find data you need from the database with higher accuracy and speed.
Boost employee productivity
Employees often spend critical time finding information and analyzing the findings to make decisions. That may result in dissatisfaction with their work and can lower their productivity. AI software is here to help. They can help collect data and summarize information quickly. For example, new AI-powered applications can perform semantic searches or text summarization to assist employees and enhance productivity.
Enhance customer experience
Manual tasks consume employees' time and hinder company growth. AI systems can automate various human resourceful tasks, allowing employees to work on tasks that are more important for success. For example, employees can cut time spent on writing summaries of meetings, comparing long insurance documents to provide the best quotes, and much more.
What Are Artificial Intelligence Examples?
Text Generation
Text generation is the most known AI application. It is a process where different machine learning models, such as large language models, learn patterns from large data sets and use them to produce high-quality human-like content. This technology can answer customers' queries in chatbots, write emails, or fix grammar.
Image Generation
Image generation familiars by open Dalle enable us to create realistic or styled images using our imagination. For example, create images for blogs, social media posts, or advertising. Many tools can even restore or generate additional parts for existing images with high precision for accuracy.
Speech Generation and Recognition
Speech generation means converting human text into spoken words and spoken words into text. These technologies make it easier to communicate with users with disabilities and improve overall user experience. For example, you can use these to convert blog articles to podcasts, make virtual assistants from past customers, or provide better accessibility when needed.
Code Generation
Code generation AI tools are famous for Microsoft 365 Copilot and GitHub Copilot. They allow coders to generate code, fix bugs, and enhance collaboration. Some tools even claim to increase developers productivity by up to 10-50%. However, these tools do not understand what they create. As a result, they often create unreliable code that needs considerable time to be corrected. That is counterproductive. Maybe that is what AGI can solve, or maybe not, but we will have to wait and see.
Video Generation
Video generation is one of the newest AI applications, utilizing Text-to-video models to create videos from text or data. It removes the need to record and edit video clips manually, which can take a lot of time. This accelerates video marketing production while minimizing human manual intervention. For example, you can use an AI-driven video generation tool to create short explainer videos about your product and share them across social media channels such as YouTube, Linkedin, Twitter, etc.
Automatic CI/CD
The ConfidentialMind stack includes build in CI/CD. It connects to your git repository and automatically deploys the needed LLMs and your backend micro services.
Documentation
ConfidentialMind AI Services
ConfidentialMind is designed for software developers, making it easy to bring generate AI capabilities to your environment. So you can build RAG applications and AI agents securely.
Al systems via AI APIs
Easily deploy generative AI systems, such as RAG and applications via AI APIs
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Add AI capabilities to your new or existing products and connect with your enterprise data effortlessly
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Protect your most valuable data, but get value from it
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