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Hey Futurists and AI Aficionados! Buckle up because were about to jump into a time machine and peek into the next decade of AI and Data Science. If you think what weve seen so far is mind-blowing, you aint seen nothin yet! So, lets put on our prediction hats and delve into the trends that will shape the AI landscape in the upcoming years.

First off, a reality check. AI has been the buzzword for years, and while weve made some amazing strides (Hello, self-driving cars and personalized medicine!), were not living in a sci-fi movie yet. No, robots arent taking over the world, but they are about to make our lives much more interesting!

Currently, most AI algorithms need robust servers and data centers to function. But what if that could change? Were looking at a future where AI models will run efficiently on your devices yes, your smartphone could soon be your AI assistant in a much more sophisticated way than Siri or Alexa could ever be!

Heres a big one: using AI to combat climate change. Algorithms are already getting better at predicting weather patterns, analyzing soil health, and tracking endangered species. But in the next decade, AI will play a crucial role in resource optimization and perhaps even in engineering solutions to reverse environmental damage. The planets heroes may be lines of code!

Currently, most AI falls under narrow or specialized intelligence good at one thing but pretty useless otherwise. However, were inching closer to Artificial General Intelligence (AGI), where machines can understand, learn, and apply knowledge across different domains. Imagine an AI that can compose music, diagnose diseases, and manage city traffic while teaching itself quantum physics!

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What's Next in AI? Predicting the Trends for the Upcoming Decade - DataDrivenInvestor

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Vlad Hu began his career as a software engineer and eventually founded his own software agency, but over the past year, the big work opportunity has been freelance artificial intelligence expert gigs. Hu isn't alone. The rise of generative AI is rapidly reshaping the freelance tech job market, with AI-related job posts from employers and searches among job seekers surging across career and freelance job platforms, including LinkedIn, Upwork and Fiverr.

Three years ago, becoming an AI expert would involve "deep knowledge in machine learning algorithms, deep learning AI in general, and a lot of technical things," said Hu, who works through Fiverr on chatbot implementation projects.

According to data from Indeed, generative AI-related job posts have increased on its platform nearly 250% from July 2021 to July 2023.

According to LinkedIn which is owned by Microsoft, OpenAI's primary investment backer since the large language model first broke through with the public in November 2022, LinkedIn member searches on gen AI terms continue to grow. Since early April, the number of U.S. LinkedIn member posts mentioning gen AI keywords has increased 25% month over month. By June, AI keywords like "ChatGPT," "prompt engineering," and "prompt crafting," were being added to profiles 15 times more frequently than at the beginning of the year.

"Many companies are exploring ways to integrate AI into their business platforms and working with skilled freelance developers," said a Fiverr spokeswoman.

Hu said businesses interested in introducing a ChatGPT or similar AI bot to an app often contact him to understand the technology. Fiverr also has seen an explosion of interest in AI-related video creation over the past six months, according to the spokeswoman, as well as hiring firms searching for AI app development experts.

Demand for AI freelance experts should continue to grow, according to LinkedIn, with a June survey it conducted among executives finding that 44% in the U.S. intend to expand their use of AI technologies in the next year; 47% say they expect it will increase productivity.

"AI is already driving changes in the workforce," wrote Dr. Karin Kimbrough, chief economist at LinkedIn, in a recent report which found just under half of executives say AI will increase productivity. "In the past year, we've seen professionals globally adopting AI skills at a rapid rate; this is happening in parallel with employers increasingly looking for talent with knowledge of how to effectively use new AI technologies to enhance productivity in their organizations."

There is opportunity for freelancers expert in AI to take advantage of the lack of AI skills among existing industry professionals, across sectors of the economy. In the U.S. job market, for example, what LinkedIn classifies as the technology, information, and media sector has the most members proficient in AI, at just 2.2%. Other industries are experiencing rapid adoption of AI core competencies, including retail and financial services, but off a very low base percentage of current employees who are proficient.

Freelance job platform Upwork, which recently signed a deal with OpenAI to connect businesses with experts familiar with its large language models, says the total number of AI skills being marketed by experts is upwards of 250. According to Margaret Lilani, Upwork's vice president of talent solutions, although there are multiple pathways to AI consulting, a strong foundation in computer science, knowledge of machine learning algorithms, proficiency in programming languages like Python, or experience in data management and analysis are often needed across job tasks.

Many AI experts also have related college degrees or experience, such as a bachelor's or master's degree in fields including computer science or engineering. Even so, "ultimately landing work within the AI space comes down to showcasing that you have the skills, ability and expertise to take on a particular project," Lilani said.

At online learning company Udacity, there has been an increase of 33% over the past year in interest for AI-based courses deep learning, AI programming with python, AI for trading, machine learning DevOps engineer, computer vision, and natural language processing among the in-demand courses."To meet this demand, roughly 20% of our current content development roadmap includes Generative AI and Generative AI-related content," said Victoria Papalian, general manager of Udacity's consumer division.

For those not yet in the job market and interested in the AI field, Lilani suggests getting an early start by taking classes in computer science. She says a foundation will be built in the programming languages needed for AI expertise, especially for high school students looking to become familiar with the building blocks of many AI fields. She added that independent methods of education, including YouTube videos or blogs focused on AI skills, are becoming more sought after in the workforce.Learning new concepts and tools like ChatGPT will become important as all types of professionals across industries advance in their careers.

Hu said to start with the basics, including use of OpenAI tools, like ChatGPT, ChatGPT API, Dall-E, and davinci.But he added that proficiency in these areas of AI is just the start. Spending time determining how to use these tools in business is critical. AI's value is limited by a user's application of the technology, so knowledge needs to be supplemented with intention for its use."It's how you bridge the gap with the real world problem that really matters," Hu said.

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AI expert is a hot new position in the freelance jobs market - CNBC

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The rise of powerful generative AI tools like ChatGPT has been described as this generations iPhone moment. In March, the OpenAI website, which lets visitors try ChatGPT, reportedly reached847 million unique monthly visitors. Amid this explosion of popularity, the level of scrutiny placed on gen AI has skyrocketed, with several countries acting swiftly to protect consumers.

In April, Italy became the first Western country toblockChatGPT on privacy grounds, only to reverse the ban four weeks later. Other G7 countries areconsidering a coordinated approachto regulation.

The UK will host thefirst global AI regulation summitin the fall, with Prime Minister Rishi Sunak hoping the country can drive the establishment of guardrails on AI. Itsstated aimis to ensure AI is developed and adopted safely and responsibly.

Regulation is no doubt well-intentioned. Clearly, many countries are aware of the risks posed by gen AI. Yet all this talk of safety is arguably masking a deeper issue: AI bias.

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Although the term AI bias can sound nebulous, its easy to define. Also known as algorithm bias, AI bias occurs when human biases creep into the data sets on which the AI models are trained. This data, and the subsequent AI models, then reflect any sampling bias, confirmation bias and human biases (against gender, age, nationality, race, for example) and clouds the independence and accuracy of any output from the AI technology.

As gen AI becomes more sophisticated, impacting society in ways it hadnt before, dealing with AI bias is more urgent than ever. This technology isincreasingly usedto inform tasks like face recognition, credit scoring and crime risk assessment. Clearly, accuracy is paramount with such sensitive outcomes at play.

Examples of AI bias have already been observed in numerous cases. When OpenAIs Dall-E 2, a deep learning model used to create artwork, wasasked to create an imageof a Fortune 500 tech founder, the pictures it supplied were mostly white and male. When asked if well-known Blues singer Bessie Smith influenced gospel singer Mahalia Jackson, ChatGPTcould not answer the question without further prompts, raising doubts about its knowledge of people of color in popular culture.

Astudyconducted in 2021 around mortgage loans discovered that AI models designed to determine approval or rejection did not offer reliable suggestions for loans to minority applicants.These instances prove that AI bias can misrepresent race and gender with potentially serious consequences for users.

AI that produces offensive results can be attributed to the way the AI learns and the dataset it is built upon. If the data over-represents or under-represents a particular population, the AI will repeat that bias, generating even more biased data.

For this reason, its important that any regulation enforced by governments doesnt view AI as inherently dangerous. Rather, any danger it possesses is largely a function of the data its trained on. If businesses want to capitalize on AIs potential, they must ensure the data it is trained on is reliable and inclusive.

To do this, greater access to an organizations data to all stakeholders, both internal and external, should be a priority. Modern databases play a huge role here as they have the ability to manage vast amounts of user data, both structured and semi-structured, and have capabilities to quickly discover, react, redact and remodel the data once any bias is discovered. This greater visibility and manageability over large datasets means biased data is at less risk of creeping in undetected.

Furthermore, organizations must train data scientists to better curate data while implementing best practices for collecting and scrubbing data. Taking this a step further, the data training algorithms must be made open and available to as many data scientists as possible to ensure that more diverse groups of people are sampling it and can point out inherent biases. In the same way modern software is often open source, so too should appropriate data be.

Organizations have to be constantly vigilant and appreciate that this is not a one-time action to complete before going into production with a product or a service. The ongoing challenge of AI bias calls for enterprises to look at incorporating techniques that are used in other industries to ensure general best practices.

Blind tasting tests borrowed from the food and drink industry, red team/blue team tactics from the cybersecurity world or the traceability concept used in nuclear power could all provide valuable frameworks for organizations in tackling AI bias. This work will help enterprises to understand the AI models, evaluate the range of possible future outcomes and gain sufficient trust with these complex and evolving systems.

In previous decades, talk of regulating AI was arguably putting the cart before the horse. How can you regulate something whose impact on society is unclear? A century ago, no one dreamt of regulating smoking because it wasnt known to be dangerous. AI, by the same token, wasnt something under serious threat of regulation any sense of its danger was reduced tosci-fi filmswith no basis in reality.

But advances in gen AI and ChatGPT, as well as advances towards artificial general Intelligence (AGI), have changed all that. Some national governments seem to be working in unison to regulate AI, while paradoxically, others are jockeying for position as AI regulators-in-chief.

Amid this hubbub, its crucial that AI bias doesnt become overly politicized and is instead viewed as a societal issue that transcends political stripes. Across the world, governments alongside data scientists, businesses and academics must unite to tackle it.

Ravi Mayuram is CTO of Couchbase.

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As regulators talk tough, tackling AI bias has never been more urgent - VentureBeat

We are seeing a progression of Generative AI applications powered by large language models (LLM) from prompts to retrieval augmented generation (RAG) to agents. Agents are being talked about heavily in industry and research circles, mainly for the power this technology provides to transform Enterprise applications and provide superior customer experiences. There are common patterns for building agents that enable first steps towards artificial general intelligence (AGI).

In my previous article, we saw a ladder of intelligence of patterns for building LLM powered applications. Starting with prompts that capture problem domain and use LLM internal memory to generate output. With RAG, we augment the prompt with external knowledge searched from a vector database to control the outputs. Next by chaining LLM calls we can build workflows to realize complex applications. Agents take this to a next level by auto determining how these LLM chains are to be formed. Let's look in detail.

A key pattern with agents is that they use the language understanding power of LLM to make a plan on how to solve a given problem. The LLM understands the problem and gives us a sequence of steps to solve the problem. However, it doesn't stop there. Agents are not a pure support system that will provide you recommendations on solving the problem and then pass on the baton to you to take the recommended steps. Agents are empowered with tooling to go ahead and take the action. Scary right!?

If we ask an agent a basic question like this:

Human: Which company did the inventor of the telephone start?

Following is a sample of thinking steps that an agent may take.

Agent (THINKING):

Agent (RESPONSE): Alexander Graham Bell co-founded AT&T in 1885

You can see that the agent follows a methodical way of breaking down the problem into subproblems that can be solved by taking specific Actions. The actions here are recommended by the LLM and we can map these to specific tools to implement these actions. We could enable a search tool for the agent such that when it realizes that LLM has provided search as an action, it will call this tool with the parameters provided by the LLM. The search here is on the internet but can as well be redirected to search an internal knowledge base like a vector database. The system now becomes self-sufficient and can figure out how to solve complex problems following a series of steps. Frameworks like LangChain and LLaMAIndex give you an easy way to build these agents and connect to toolings and API. Amazon recently launched their Bedrock Agents framework that provides a visual interface for designing agents.

Under the hood, agents follow a special style of sending prompts to the LLM which make them generate an action plan. The above Thought-Action-Observation pattern is popular in a type of agent called ReAct (Reasoning and Acting). Other types of agents include MRKL and Plan & Execute, which mainly differ in their prompting style.

For more complex agents, the actions may be tied to tools that cause changes in source systems. For example, we could connect the agent to a tool that checks for vacation balance and applies for leave in an ERP system for an employee. Now we could build a nice chatbot that would interact with users and via a chat command apply for leave in the system. No more complex screens for applying for leaves, a simple unified chat interface. Sounds exciting!?

Now what if we have a tool that invokes transactions on stock trading using a pre-authorized API. You build an application where the agent studies stock changes (using tools) and makes decisions for you on buying and selling of stock. What if the agent sells the wrong stock because it hallucinated and made a wrong decision? Since LLM are huge models, it is difficult to pinpoint why they make some decisions, hence hallucinations are common in absence of proper guardrails.

While agents are all fascinating you probably would have guessed how dangerous they can be. If they hallucinate and take a wrong action that could cause huge financial losses or major issues in Enterprise systems. Hence Responsible AI is becoming of utmost importance in the age of LLM powered applications. The principles of Responsible AI around reproducibility, transparency, and accountability, try to put guardrails on decisions taken by agents and suggest risk analysis to decide which actions need a human-in-the-loop. As more complex agents are being designed, they need more scrutiny, transparency, and accountability to make sure we know what they are doing.

Ability of agents to generate a path of logical steps with actions gets them really close to human reasoning. Empowering them with more powerful tools can give them superpowers. Patterns like ReAct try to emulate how humans solve the problem and we will see better agent patterns that will be relevant to specific contexts and domains (banking, insurance, healthcare, industrial, etc.). The future is here and technology behind agents is ready for us to use. At the same time, we need to keep close attention to Responsible AI guardrails to make sure we are not building Skynet!

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Decoding Opportunities and Challenges for LLM Agents in ... - Unite.AI