This three-part AI blog series will explore basic AI concepts, AI and the environment, and AI in Utilities.
Let’s start with a brief history lesson!
AI (Artificial Intelligence)’s origins can be traced a ways back, but modern AI really began in the mid-20th century with figures like Alan Turing. However, in the 1950s, computers needed to be more advanced and were too expensive for widespread AI development. Funding increased in the 1960s and 1970s, including USA’s DARPA (Defense Advanced Research Projects Agency) government support. Significant developments in the 1980s included “deep learning” and expert systems. By the 2000s, AI gained popularity through various applications like IBM’s Deep Blue and Apple’s Siri. Today, AI technology is flourishing with autonomous vehicles, machine learning tools, chatbots, virtual assistants, and the growing Internet of Things (IoT), providing numerous new use cases.
As of now, there are different types of AI based on type, learning approach, and complexity of tasks which can be broadly broken down as follows:
- Reactive Machines: These basic AI systems react to current scenarios but cannot use past experiences. They behave in a set manner and are considered reliable. Examples include spam filters, Netflix recommendation engines, and chess-playing supercomputers like Deep Blue.
- Limited Memory: Limited memory AI is a supervised system that learns from past data and experiences to create models. It uses historical data for predictions and can observe and learn from its environment to make better decisions over time. ChatGPT, like other GPT models, can be considered limited memory AI. It lacks traditional memory storage, but its self-attention mechanism allows it to retain context from previous parts of the conversation, making it suitable for multi-turn dialogue-based tasks. ChatGPT builds up a representation of the conversation’s history during a chat session to generate coherent and contextually relevant responses to user queries.
- Theory of Mind (ToM): Theory of Mind AI aims to mimic human-like thoughts, emotions, and decision-making. This AI type comprehends human behavior and can interpret facial signals, replicate feelings, and respond accordingly. Achieving a full Theory of Mind in AI is challenging due to the complexity of understanding human emotions accurately.
- Self-Awareness: Self-aware AI goes beyond ToM and involves machines with consciousness on par with human levels. It is aware of its thoughts and reactions, making it a highly advanced and theoretical concept. Successful development of self-aware AI would lead to artificial superintelligence.
These categories represent the evolution of AI from simple reactive machines to potentially self-aware systems, showcasing the wide-ranging capabilities and applications of artificial intelligence.
For the day-to-day, we use AI more often than we think. Here are some basic examples of AI used in everyday life:
- Virtual Assistants: Virtual assistants like Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana use AI to understand natural language and provide helpful responses. They can answer questions, set reminders, control smart home devices, play music, and perform various tasks based on voice commands.
- Recommendation Systems: AI-powered recommendation systems are prevalent on streaming platforms like Netflix, YouTube, and Spotify. They use machine learning algorithms to analyze users’ preferences and behavior, suggesting movies, videos, or music that match their interests.
- Smartphones and Smart Cameras: AI is integrated into modern smartphones to enhance photography by offering features like automatic scene recognition, portrait mode, and night mode. Smart cameras can detect faces and smiles, ensuring better photos.
- Social Media Algorithms: Platforms like Facebook, Instagram, and Twitter use AI algorithms to curate users’ feeds, displaying content they are more likely to engage with based on past interactions and interests.
- Email Spam Filters: Most email services utilize AI to filter out spam and detect phishing attempts, helping users manage their inboxes more effectively.
- Ride-Hailing Apps: Apps like Uber and Lyft use AI algorithms to match riders with drivers, determine the optimal routes, and estimate travel times and prices.
- Language Translation: AI-powered language translation services, such as Google Translate, facilitate communication across different languages, making travel and international communication easier.
- Autocomplete and Text Prediction: AI is used in various applications, including search engines and text messaging platforms, to provide autocomplete suggestions and predictive text.
- E-commerce Personalization: Online shopping platforms use AI to provide personalized product recommendations based on users’ browsing and purchase history.
- Chatbots and Customer Support: Many websites and customer service centers employ AI-driven chatbots to interact with users and answer frequently asked questions.
These examples demonstrate how AI is now integral to our daily lives, making our interactions with technology more seamless and personalized. We expect even more innovative and useful applications to become commonplace as AI technology advances.
At Awesense, we use limited-memory AI, specifically a type of machine learning, to process utility data with our AI Data Engine. The Awesense AI Data Engine ingests data sets from disparate data sources, structures them according to our Energy Data Model, and cleanses the structured data. The machine learning capacity of the AI Data Engine has the capacity to solve major errors in time series and geospatial data alike and make predictions or decisions on how to resolve those errors. The engine will provide a record of errors found and corrected. Depending on the data sets ingested; the collated data sets result in a digital twin or digital simulation of the electricity grid.
The second part of our AI blog series will explore the environmental impacts of AI technology. Click here for part two.