Conversational AI versus AI Data Science
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Artificial intelligence (AI) refers to computer systems performing tasks that typically require human intelligence, with advancements leading to deep learning capabilities that allow problem-solving and independent creation. Conversational AI enables devices to interact through voice commands, answering questions and managing tasks, while deep learning focuses on analyzing data to make accurate predictions. AI has significant implications for enterprises, allowing for predictive analytics based on customer behavior data to proactively address potential issues.
Conversational AI versus AI Data Science
- 1. Conversational AI vsAI Data Science AIs for the Enterprise
- 2. What is Artificial Intelligence? Artificial intelligence is the theory that computer systems are able to perform tasks that normally require human intelligence/ a human brain for things like decision making, speech recognition and visual perception. With the invention and development of artificial intelligence, several tasks that require a human to perform, have now been handed over to a computer – which can now process information similar to that of a human brain. There are several forms of Artificial Intelligence. We’ve surpassed artificial intelligence and have begun moving into what we refer to as deep learning. Deep learning is the ability for computers to not only process information, but to problem solve and create on their own, based on learned actions from human intelligence.
- 3. Conversational AI You talk to your device and it talks back – answering questions, finding information for you and setting appointments to your calendar. This is conversational AI Learning your voice command, interpreting the command and delivering a result based on the information gathered. Conversational AI was the beginning of using artificial intelligence tools The use of Digital Assistants: Siri Alexa Cortana Google Home Digital Assistants are machine learning systems that work on an interactive level, and make informed guesses at what is being asked of them in the moment
- 4. How does a Machine have a Conversation? Information is “learned” and stored in a database of answers. When a question is asked to the Digital Assistant, they access and sort through the stored information in order to arrive at a foregone conclusion. They work on an interactive level, and make informed guesses at what is being asked of them in the moment. Conversational AIs answers questions based on past or present data.
- 5. Conversations with Digital Assistants: What is the temperature outside? What is the status of current traffic conditions? What is the Dow Jones Industrial Average? What is the current stock price for my company? Set my alarm for 8 am Add the meeting with company X to my calendar for tomorrow at 3 pm Call Jane Remind me to get my papers when I get home Conversational AIs Can Do: Conversational AIs Can Answer:
- 6. What Answers Aren’t Known? Higher levels of this type of machine learning are sometimes referred to as “cognitive” – implying that the machine is “thinking”. “Thinking” is referred to accessing stored information. Conversational AI’s can’t answer the following: What will my investment yield in 5 years? How much do I need in my bank account to live for 2 years? What kind of impact will an increase in interest rates have on the stock market?
- 7. Conversational AI vs Deep Learning AI Deep Learning is a type of machine learning that analyzes data, patterns, and trends, and then makes accurate predictions about the future based on the data provided. Deep Learning is a learning that goes beyond understanding what a question is and coming up with a known answer. Deep Learning works with neural networks and hidden layers to create a predictive system that can reach extremely high levels of accuracy.
- 8. New Kind of AI: AI Data Science One hundred data scientists can handle 100x as much data, but since you can’t connect the brains of human data scientists, sharing all of their information can be difficult. Data is coming in faster than a human brain can analyze it, therefore new solutions are needed to process data. At Razorthink, we are using deep learning to sort through data on several hundred levels according to a wide range of specific identifiers and algorithms to reveal patterns that can then be utilized to predict future realities. Providing the ability to uncover real- time insights within massive feeds of data - such as customer behavior data.
- 9. What This Means to Enterprise: By applying AI to customer behavior Data Science, it is possible to predict problems that customers will have - even before they occur - and take steps to avoid the problem altogether. Such analysis can also be used for predicting in real-time the type of offers that specific customers will use, based on behavior rather than demographics.