Serendipity35

ChatGPT has received a lot of attention for about a year, and it has also garnered competition. Google's entry into the AI for the masses is Gemini which has excellent web browsing and Google app integrations. Gemini provides results with often cited sources and links and has ‘Search Related Topics’ feature under some of its results allowing you to explore other search avenues you might have not considered initially. ChatGPT's paid version crawls the web but is not as effective as Gemini and for citations you need to explicitly prompt it to do so.

Gemini and ChatGPT generate images but Gemini does it for free while ChatGPT limits this function to paid plans with access to DALL-3. Then again, ChatGPT paid version might be worth it because the quality of DALL-3 images are better than those of Gemini.

Here is a features comparison from another site.

 

I read an article from Rutgers University-Camden that begins by saying that most people now realize that artificial intelligence has become increasingly embedded in everyday life. This has created concerns. One of those lesser-spoken-about concerns is around bias in its programming. Here are some excerpts from the article.

Some AI tasks are so innocuous that users don't think about AI being involved. Your email uses it. Your online searches use it. That smart thermostat uses it. Are those uses frightening? But as its capabilities expand, so does its potential for wide-ranging impact.

Organizations and corporations have jumped on the opportunity presented by AI and Big Data to automate processes and increase the speed and accuracy of decisions large and small. Market research has found that 84 percent of C-suite executives believe they must leverage artificial intelligence to achieve their growth objectives. Three out of four believe that if they don't take advantage of AI in the next five years, they risk going out of business entirely.

Bias can cause artificial intelligence to make decisions that are systematically unfair to particular groups of people, and researchers have found this can cause real harm. The Rutgers–Camden researcher Iman Dehzangi, says that “Artificial intelligence and machine learning are poised to create valuable opportunities by automating or accelerating many different tasks and processes. One of the challenges, however, is to overcome potential pitfalls such as bias.” 

What does biased AI do? It can give consistently different outputs for certain groups compared to others. It can discriminate based on race, gender, biological sex, nationality, social class, or many other factors.

Of course it is human beings who choose the data that algorithms use and humans have biases whether they are conscious of them or not.

"Because machine learning is dependent upon data, if the data is biased or flawed, the patterns discerned by the program and the decisions made as a result will be biased, too," said Dehzangi, pointing to a common saying in the tech industry: "garbage in, garbage out." “There is not a successful business in operation today that is not using AI and machine learning,” said Dehzangi. Whether it is making financial investments in the stock market, facilitating the product development life cycle, or maximizing inventory management, forward-thinking businesses are leveraging this new technology to remain competitive and ahead of the curve. However, if they fail to account for bias in these emerging technologies, they could fall even further behind, remaining mired in the flawed data of the past. Research has revealed that if care is not taken in the design and implementation of AI systems, longstanding social biases can be embedded in the systems' logic.

.Coursera’s CEO, Jeff Maggioncalda, says leveraging AI in online learning is key to a more accessible, flexible education experience. Coursera is a major platform for free and paid, non-credit and credit learning opportunities. Remember MOOCs? The term isn't in as wide usage as it was a decade ago but Coursera was an early serious player in that space and still offers short-form training and master’s degrees from Ivy League institutions like the University of Pennsylvania.

While many in education have been worrying about how AI is and will impact teaching and learning, online providers and course designers have been more likely to embrace AI tools.

Generative AI is good at language translations and Coursera who now has 4,200 courses translated into 17 languages as AI has made the translations easier and more affordable. They have also experimented with using AI for a personalized learning companion (chatbot) named Coach where students can ask for help on a concept, to create practice problems, or summarize activities. It won’t give users the answer, especially during testing.

For course designers, it can create outlines, write learning objectives, and compile lessons into new courses.

Coursera works with partners who can make content available for free.

Natural Language Processing (NLP) is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. The "natural" part is that the goal is that this AI language use is meaningful and contextually relevant. This might be used for tasks such as language translation, sentiment analysis, and speech recognition.

Search engines leverage NLP to improve various aspects of search. Understanding what a user means when searching for a search string and understanding what the different pages on the web are about and what questions they answer are all vital aspects of a successful search engine.

According to AWS, companies commonly use NLP for these automated tasks:
•    Process, analyze, and archive large documents
•    Analyze customer feedback or call center recordings
•    Run chatbots for automated customer service
•    Answer who-what-when-where questions
•    Classify and extract text

NLP crosses over into other fields. Here are three.

Computational linguistics is the science of understanding and constructing human language models with computers and software tools. Researchers use computational linguistics methods, such as syntactic and semantic analysis, to create frameworks that help machines understand conversational human language. Tools like language translators, text-to-speech synthesizers, and speech recognition software are based on computational linguistics. 

Machine learning is a technology that trains a computer with sample data to improve its efficiency. Human language has several features like sarcasm, metaphors, variations in sentence structure, plus grammar and usage exceptions that take humans years to learn. Programmers use machine learning methods to teach NLP applications to recognize and accurately understand these features from the start.

Deep learning is a specific field of machine learning which teaches computers to learn and think like humans. It involves a neural network that consists of data processing nodes structured to resemble the human brain. With deep learning, computers recognize, classify, and co-relate complex patterns in the input data.

Overview of NLP