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As the New Year approaches, many people are addressing financial resolutions. But a significant number of Americans feel like theyre behind on achieving their money goals.

About 80 percent of Americans didnt increase their emergency savings this year, according to a recent Bankrate survey. Nearly one-third of households (32 percent) have less emergency savings now than at the start of 2023.

Generative AI has emerged as a useful tool for financial advice, offering consumers a free way to receive customized guidance on everything from creating a budget to managing an investment portfolio.

Despite a strong economy, many Americans are struggling to achieve their financial goals as 2023 comes to a close.

Nearly half of Americans are struggling to be financially secure, according to a Bankrate survey. Still, many of the Americans surveyed are optimistic about their financial future 46 percent of Americans who dont feel financially secure believe that they will someday.

About 2 in 5 Americans (41 percent) surveyed blamed insufficient retirement funds as the primary factor fueling their feelings of financial insecurity. Building an emergency savings fund is another common aspiration, yet 60 percent of Americans feel theyre behind on meeting this goal, too.

More people are now turning to AI platforms, like ChatGPT, as a cost-effective way to manage their finances. The public debut of ChatGPT in November 2022 has boosted consumer awareness of AIs potential: The chatbot currently has over 100 million users and the website generated 1.6 billion visits since June 2023.

For many Americans, their financial landscape feels like a battlefield an on-going struggle to save for major life events while combating rising prices.

While inflation is down significantly from the summer of 2021, interest rates remain at their highest level in more than 15 years. From buying a car to purchasing a home to paying down credit card debt, consumers are feeling the impact of broader economic factors on their bottom line.

Americans feel behind in achieving their financial goals due to a variety of factors:

For Americans struggling to get ahead, AI offers a way to obtain personalized advice and financial information at home for free.

AI can be a useful tool to understand how to organize basic finances like budgeting, saving, and paying down debt, says Stephanie Genkin, a certified financial planner and founder of My Financial Planner, LLC in Brooklyn, New York. While not always 100 percent reliable, its a great place to start to gain financial literacy.

In the not-so-distant past, managing money often meant sitting down with a financial advisor or conducting your own in-depth research. Information wasnt always readily available or free.

Flash forward to today, when the financial industry is experiencing a digital revolution. Consumers now have access to easy online banking, handy budgeting apps and even robo-advisors that use complex algorithms to help with investing.

While these advancements make money management more convenient and accessible, the advice they offer if any is often generic.

That lack of personalized guidance is changing with artificial intelligence, specifically AI chatbots. These digital assistants offer the potential to fill the gap between individuals struggling with financial goals and the guidance they need to achieve those goals.

Platforms like ChatGPT offer more than just casual conversations with a robot. They provide access to financial planning information and insights once only available for a fee from an advisor.

One big advantage of AI is its ability to analyze vast data sets quickly. AI can review your income, expenses, savings, investments and financial goals, offering advice tailored to your unique situation. Users can also get guidance on creating a budget or understanding insurance products.

Other AI-driven financial tools include:

Consumers are also getting more comfortable with the idea of AI-integration in financial planning. In fact, nearly 1 and 3 investors would be comfortable using generative artificial intelligence to receive financial advice, according to a report by CNBC.

However, its crucial to note that while generative AI can be a valuable tool, it cant replace human judgement. Sure, AI can analyze large amounts of data, but its not going to provide you with specific investment recommendations. Certain aspects of your financial life still require a more nuanced approach.

Also, OpenAI, the company that developed ChatGPT, warns that the chatbot sometimes writes plausible-sounding but incorrect or nonsensical answers.

For consumers, AI can enhance financial decision-making but it cant replace it. Experts recommend finding a reliable source to vet information provided by a chatbot.

I wouldnt make any big financial decisions without also speaking to a fiduciary, says Genkin.

Keep in mind: While AI chatbots are efficient tools for time-saving activities, some of the content generated can be unreliable or outdated.

Consumers arent the only ones using AI to manage money.

For years, financial firms have utilized the technology for everything from fraud detection to credit scoring. As generative AI evolves, more financial advisors are finding new ways to incorporate the technology into their workflows to streamline everyday tasks such as research, stock market analysis and report generation.

Jeremey Finger, a certified financial planner and founder of Riverbend Wealth Management in Myrtle Beach, South Carolina, says he thinks chatbots can be an efficient tool for advisors by helping them simplify tasks like drafting emails to clients.

I think the danger, especially for clients, lies in assuming the information it provides is true, says Finger. It also cant ask a client thoughtful follow-up questions. It only works off the information you put in.

For example, if someone with a disability or terminal illness fails to input those details into a chatbot, the advice they receive wont be tailored to their needs.

To assume AI is taking those things into consideration is poor judgement, says Finger.

Robo-advisor: A type of automated financial advisor that provides algorithm-driven portfolio management and investing services with little to no human intervention.

Financial advisor: A professional who is paid to offer financial advice to clients. They typically offer guidance on retirement, personal finances and investments.

Rather than turning to AI chatbots, there are other options available if you need personalized financial guidance, including traditional advisors and robo-advisors.

The rise of AI has seen a parallel surge in the popularity of robo-advisors. While not a new concept, robo-advisors have become more sophisticated with the integration of AI, offering users a cheaper and more convenient way to invest.

But creating a comprehensive financial plan involves more than a data-driven investment strategy. Selecting the right financial advisor, whether human or AI-driven, is an important step in achieving financial goals.

Not everyone needs to work with a human advisor, but doing so provides valuable insight and context you might not get with generative AI or even a robo-advisor. Estate planning, which involves drafting legally-binding documents to pass along your assets after you die, is one example of a complex situation that warrants speaking to a human advisor.

But how do you select the right financial advisor? Here are a few tips:

If you need expert guidance when it comes to managing your money or planning for retirement, Bankrate can help you get matched with a financial advisor in minutes.

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A financial advisor provides guidance to help clients manage their money and plan for their financial future. They help track, manage and balance investments as well as offer advice on topics like retirement planning, insurance, buying a home and budgeting.

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Generative models, like ChatGPT, produce human-like responses and can assist in a range of tasks, including financial planning.

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You can set yourself up for success by making your goals specific, measurable and achievable. For example I want to make more money isnt a specific goal, but I want to increase my salary 30 percent over the next three years is.

Once youve defined your goal, dont just set it and forget it. Designate specific times to check your accounts and make adjustments as needed. Its generally recommended to review your progress at least once a month for short-term goals and once or twice a year for long-term goals.

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Using Generative Artificial Intelligence As A Financial Tool - Bankrate.com

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Prediction: This Will Be the Best Artificial Intelligence (AI) Stock to Own in 2024  The Motley Fool

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Prediction: This Will Be the Best Artificial Intelligence (AI) Stock to Own in 2024 - The Motley Fool

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A New Artificial Intelligence ETF (WISE) Hits the Market  Zacks Investment Research

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A New Artificial Intelligence ETF (WISE) Hits the Market - Zacks Investment Research

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The Intel Core Ultra mobile processor family brings AI to PCs, while 5th Gen Intel Xeon processors offer AI acceleration for data centers.

Today at the Intel AI Everywhere event in New York City, Intel announced the general availability of the Intel Core Ultra mobile processor family, delivering faster AI performance for graphic designers, smart factories and more. The 5th Gen Intel Xeon processor family, coming next year to OEMs and cloud service providers, has AI acceleration in all of its cores. Plus, Intel is making progress with manufacturing its Gaudi3 AI accelerator.

Intels overall goal is to proliferate what Intel is calling AI PCs, meaning on-chip AI for laptops and data centers, allowing more hardware to run generative AI more efficiently. Acer, Asus, Lenovo, LG, Dell, HP, MSI, Samsung and more laptop makers include Intel chips in their devices.

Intel is on a mission to bring AI everywhere through exceptionally engineered platforms, secure solutions and support for open ecosystems, Intel CEO Pat Gelsinger said in a press release.

Jump to:

The key differentiator of the Intel Core Ultra mobile processor family (Figure A) is the on-chip AI accelerator, or neural processing unit; this makes these products especially suitable for running generative AI locally, Intel said.

According to Intel, the Intel Core Ultra mobile processor family has 2.5x better power efficiency than the previous generation. Its world-class Intel Arc GPU and CPU are each capable of speeding up AI solutions; this required a massive change in how Intel assembles its microchips. The Intel Core Ultra family is manufactured using the new Intel 4 process with extreme ultraviolet.

Figure A

Intel worked with more than 100 software vendors to bring AI-boosted applications to the market that run well on Intel Core Ultra, including Adobe Premiere Pro.

Intel Core Ultra-based AI PCs are generally available today from Acer, ASUS, Dell, Dynabook, Gigabyte, Google Chromebook, HP, Lenovo, LG, Microsoft Surface, MSI and Samsung in the U.S.

The 5th Gen Intel Xeon processor family (Figure B) is a data center processor with built-in AI acceleration, optimized to run large language models like GPT-4 or Llama 2. The 5th Gen Intel Xeon processor, offers up to 42% higher inference and fine-tuning on models as large as 20 billion parameters, Intel said.

Figure B

5th Gen Intel Xeon processors achieved up to 2.7x better query throughput on the IBM watsonx.data platform compared to previous-generation Xeon processors during testing, Intel said. Google Cloud plans to deploy 5th Gen Xeon next year. 5th Gen Intel Xeon processors will be generally available in Q1 2024 from certain OEMs including Cisco, Dell, HPE, IEIT Systems, Lenovo, Super Micro Computer and others. Major cloud service providers are expected to launch 5th Gen Xeon processor-based instances throughout 2024.

SEE: 5th Gen Xeon processors and the AI PC concept were first announced by Intel in September. (TechRepublic)

The Intel Gaudi3 is an accelerator for deep learning and large-scale generative AI models. At the AI Everywhere event, Gelsinger showed a Gaudi3 AI accelerator for the first time in public and said the Gaudi3 is now out of fab.

The company is on track to release the Intel Gaudi3 AI accelerator next year.

When we think about technology, is it good or bad? said Gelsinger at the AI Everywhere event. Its mostly neutral. Its our job to shape it into a force for good.

Gelsinger predicts more AI inference will happen at the edge in the future. A small number of companies will train AI, but many more will perform inferencing on models that need to run locally and as close as possible to devices. Thats the world Intel wants to release its AI PCs into, putting large language models into every PC.

Note: TechRepublic is covering the Intel AI Everywhere event virtually.

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Intel Puts Artificial Intelligence First With Processor Announcements - TechRepublic

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It is expected that by 2050, the Earths population will grow to 10 billion people or more. While it is possible that some limited new land can be brought under cultivation, it is clear that the worlds farmers will have to produce far more food with only scant increases of tillable land. Artificial Intelligence (AI) will be the farmers tool in achieving this goal.

As described in an article published by the University of Florida[1], farmers can now collect and analyze vast amounts of data about their crops. This includes weather patterns, soil health, and plant growth. Through the use of AI, modern farmers can improve their methods, making it possible to improve their crop yields, reduce waste and ultimately increase profitability.

As we face the daunting challenge of feeding a burgeoning global population, the role of AI in agriculture becomes increasingly pivotal. According to experts from the University of Florida:

"With the help of AI to optimize farming practices, farmers can improve their crop yields, reduce waste, and increase profitability. Accurate and timely data can make the difference between a successful or unsuccessful crop cycle, making it crucial for farmers to leverage technology and data to their advantage."

This insight highlights the critical role of AI in enhancing agricultural productivity and sustainability, essential for meeting the food demands of a growing population with limited arable land.

A critical challenge faced by farmers is sustainability. Forbes[2] reports that fully eleven percent of global emissions come from agriculture and that almost 40% of food produced in the US ends up being wasted. We also learn that just shy of two-thirds of the antibiotics used today are used to treat livestock that are ultimately intended to feed people and not to treat people and their illnesses directly. This is a direct cause of antibiotic resistance that is now emerging as a major factor in healthcare. Agricultural AI can go a long way towards alleviating these issues and more.

Agricultural artificial intelligence is a fast-evolving field, with significant new announcements out almost daily. Lets look at some of the AI generalities that also apply specifically to agricultural AI and also at some of whats even now available for todays farmers.

Machine learning is an essential part of AI, including agricultural AI. An article by MIT[3] quotes AI pioneer Arthur Samuel, defining machine learning as the field of study that gives computers the ability to learn without explicitly being programmed.

Traditional computer programming requires creating complete instructions for a computer to follow based on clearly defined inputs in order to accomplish a task. Machine learning, on the other hand, involves allowing computers to program themselves.

The first step is to garner a vast amount of data relevant to the need. In general, the inputs might be repair statistics, sensor data, or repair reports. The next step is to study the observed cause-and-effect relationships between the data elements in a dizzying array of combinations. Machine learning then puts the pieces together, and based on the patterns observed, it can predict future relationships with a high degree of reliability.

One of the things machine learning can do is to distinguish a tiny weed from a tiny plant.

TheLASERWEEDERscours a farmers land and employs AI to determine what each entity it encounters is a nascent crop plant or a weed. The machine, described as a mobile data center, can determine the difference between 40 crops and 80 types of weeds. If a weed is detected, a killer laser destroys the weed in milliseconds.

As described in a video fromCarbon Robotics [4], this AI-based device can take the drudgery out of farm work. NBC describes it as a killer robot with an AI brain, but its victims arent enemy combatants; rather, its targets are weeds!

LASERWEEDERvideo 2 min 3 seconds

The device, costing $1.2 million, can work 24 hours a day and can replace 30 farm workers who are becoming increasingly difficult to find and hire. In some ways, it can outperform human workers in that it finds immature weedlings that are too small for the workers to deal with.

Because the LASERWEEDER destroys weeds with lasers and not with pesticides, its a natural option for organic farmers. Its also reported that the data gleaned from the weed-killing process will be invaluable to farmers, providing them with real-world insights that will allow them to produce more food economically.

The LASERWEEDER, a brainchild of Carbon Robotics, is redefining weed control in agriculture. Here's the tech magic behind it:

High-Tech Vision and Precision: The LaserWeeder uses 42 high-resolution cameras combined with state-of-the-art computing to distinguish between crops and weeds in real-time. This isn't just a camera snapping pictures; it's a sophisticated AI system with deep-learning-based computer vision models that can tell a weed from a crop with sub-millimeter accuracy.

Laser Power: Armed with 30x 150W CO2 lasers, the LaserWeeder is ready to fire every 50 milliseconds. Imagine the precision and speed it's like having a sniper in the field, targeting only the bad guys (weeds, that is) and leaving the good guys (crops) untouched.

Environmental Benefits: This isn't just about zapping weeds. The LaserWeeder's method is a leap towards sustainable farming. By using lasers instead of chemicals, it leaves the soil microbiology undisturbed, unlike traditional tillage. This means healthier soil, healthier crops, and a happier environment. Plus, it's a boon for organic farming, offering an economical path to weed control without herbicides.

Efficiency and Cost-Effectiveness: Think about the labor and cost savings. The LaserWeeder can kill up to 200,000 weeds per hour and cover 2 acres per hour at 1mph. It works day or night, in all conditions, significantly cutting down the manual labor and the variable costs associated with traditional weed control methods.

In a nutshell, the LASERWEEDER is more than just a tool; it's a revolution in agricultural practices, aligning with the goals of increased efficiency, sustainability, and environmental responsibility.

The agricultural sector is facing a significant labor challenge, especially with the growing scarcity of willing workers. This is where AI and automation step in as game-changers.

Reducing Dependence on Manual Labor: AI-driven technologies, like the LASERWEEDER, are prime examples of how automation can significantly reduce the need for manual labor in farming. These technologies are capable of performing tasks that traditionally required a large workforce, such as weeding, harvesting, and monitoring crop health.

Enhancing Efficiency and Productivity: With AI, tasks are not only done faster but also with greater precision. For instance, AI-powered drones can monitor crop health over large areas within a fraction of the time it would take humans. This efficiency translates into higher productivity and, ultimately, better yields.

Addressing Labor Shortages: In regions where there is a shortage of agricultural workers, AI and automation provide a viable solution. Automated machinery and AI systems can operate around the clock, compensating for the lack of human labor and ensuring that agricultural operations do not suffer.

Shifting the Workforce Dynamics: As AI takes over more labor-intensive tasks, the role of the agricultural workforce is evolving. There's a growing need for skilled personnel to manage, maintain, and supervise these AI systems. This shift is creating new job opportunities that focus more on technology management rather than manual labor.

In essence, AI and automation are not just addressing the labor challenges in agriculture; they are transforming the very nature of farming work. By reducing the reliance on manual labor, these technologies are paving the way for a more efficient, sustainable, and productive agricultural sector.

As described by NVIDIA[5], Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data. It works by identifying patterns and structures within existing data to generate new and original content.

The most famous example of Generative AI is, of course, ChatGPT.

As a start, farmers can use CHATgpt directly to get information. There are also specific platforms for agriculture. The Farmers Business Network[6] (FBM) describes a platform called, simply,NORM. The model concentrates on information of use to farmers. As described, examples of questions that can be asked are:

As detailed by Ambrook[7], NORM is built on OpenAIs GPT-3.5 model, and uses public data like weather reports, soil data, and product labels to answer ag-related questions. It also taps into FBNs exclusive agronomic data and assets from the USDAs National Agricultural Statistics Service.

Another example is farmer.Chat[8]. Thisvideoillustrates a farmer employing this Farmer.CHAT to get information about a problem area in his field.

Farmer.CHAT. by Gooey AI

The rest of the video illustrates the interactions between the farmer and farmer.CHAT, as the latter proposes solutions to the insect problem.

The potential of generative AI in agriculture is vast and varied. From tackling diseases to optimizing yields and revolutionizing crop breeding, AI is not just a tool but a transformative force in the agricultural sector. As technology evolves, we can expect even more innovative applications that will continue to reshape the future of farming.

Now, let's dive into how AI is not just a buzzword but a real game-changer on the farm. The folks at the University of Florida are onto something big here. They're talking about AI applications that are not just smart, but also kind to our planet:

Smart Watering Systems: Imagine a system that knows exactly when your crops are thirsty. That's what AI-driven irrigation is all about. It's like having a weatherman and a soil expert right in your field, ensuring every drop of water is used where it's needed most.

Predicting the Future of Crop Prices: Here's where AI flexes its economic muscles. By crunching numbers on climate trends and market shifts, AI helps farmers make savvy decisions. Less waste, more profit that's the kind of math farmers love.

Self-Driving Tractors? Yes, Please!: Deep learning is bringing the future to farms with autonomous tractors. These aren't your granddad's tractors they're high-tech beasts that know their way around a field, dodging obstacles and getting the job done with no coffee breaks.

Weed Zapping with Precision: Thanks to AI, we're seeing a revolution in weed control. Systems like John Deere's Blue River See & Spray are like snipers, taking out weeds with pinpoint accuracy. This means less herbicide on our food and in our environment. It's a win-win for farmers and Mother Nature.

Crop Disease Prediction:One of the most promising applications of generative AI is in the early detection and prediction of crop diseases. By analyzing vast datasets, including images of crop fields, weather patterns, and historical disease outbreaks, AI models can predict potential disease outbreaks before they become visible. This early warning system allows farmers to take preemptive actions, reducing the spread of disease and minimizing crop damage.

Yield Optimization:Generative AI is also playing a pivotal role in yield optimization. It can analyze data from various sources soil quality, weather conditions, crop health to generate recommendations for optimal planting, irrigation, and harvesting times. This not only maximizes yield but also ensures efficient use of resources.

Customized Crop Cultivation Plans:Another exciting application is the creation of customized crop cultivation plans. Generative AI can process data specific to a farmer's land, such as soil type, microclimate, and previous crop cycles, to generate tailored farming strategies. This personalized approach can significantly boost productivity and sustainability.

Enhancing Genetic Crop Improvement:In the field of genetic crop improvement, generative AI stands as a transformative force. It has the capability to simulate countless genetic combinations, enabling it to forecast traits that enhance crop resilience, nutritional value, and yield. This advanced approach significantly speeds up the breeding process, facilitating the creation of superior crop varieties more efficiently than conventional methods.

So, there you have it. AI in agriculture is more than just a fancy term it's making farming smarter, more sustainable, and, dare I say, cooler. It's not just about growing more food; it's about growing food the right way.

As described in an article posted by the University of Arkansas in The Arkansas Journal of Social Change and Public Service[9], 73% of the crop farmworker population in the United States are immigrant workers, and about 48% of hired crop farm workers have no work authorization.

The lack of willing workers, foreign, let alone US citizens, is a tremendous problem facing American agriculture. The answer is automation, and the 185-year-old John Deere company is jumping full steam into the AI revolution.

Simplifying agricultural automation is the fact that, like the factory floor, a farm is a work environment where everyone knows their job and both act and react in proscribed ways. This is entirely unlike the bedlam of busy city streets. So, while self-driving cars are going nowhere fast, the farm is a far more advantageous environment for driverless vehicles.

John Deeres autonomous 8R Farm Tractor.

As described in a report published by CNBC[10], John Deere has taken up the challenge presented by this opportunity in the form of its 8R Farm Tractor, which doesnt need a driver and instead relies on AI. Deere has curated hundreds of thousands of images from different farm locations and under various weather and lighting conditions so that with machine learning, the tractor can understand what its seeing and react accordingly.

No matter if youre a general or a farmer, you dont want to carpet bomb your opponent. If youre a general, you want to eliminate enemy soldiers, not civilians. If youre a farmer, you want to destroy weeds, not food crops.

Agricultural AI will make it easier to efficiently eliminate weeds, in some cases without any pesticides at all. In other cases, when pesticides cant be completely avoided, agricultural AI will make it possible to only hit the weeds and avoid the food crops.

This is important for two obvious reasons. The first is that pesticides cost money. The second is that the fewer pesticides that are used in the vicinity of food crops, the more money the farmer can get for his produce. And, of course, most agree that pesticide-free food is just plain healthier.

As the cost of AI comes down, and as more and more farmers worldwide have access to it, the world will enjoy better, cheaper and healthier foods. And let's not forget that by enabling more automated farming processes, Agricultural AI will mean that fewer farm workers will be stoop laborers, while more and more of them will emerge as machine operators, repair people and network technicians.

Perhaps the greatest modern breakthrough in agriculture was brought on by the tragic German scientist Fritz Haber, who pioneered a process to mass produce ammonia, which in turn can be used to produce artificial fertilizer. Before this development, the population of the world was less than two billion, and now it is four times that much, despite the ghastly toll of 20th century wars.

As much as we talk about the new industrial revolution, we may now acknowledge a new revolution in farming, brought on by artificial intelligence, but unlike the Haber Process, which uses vast amounts of energy and is extremely environmentally unfriendly, the AI revolution is essentially a clean revolution that will allow farmers to do more with less.

It will either reduce pesticide use or eliminate it entirely. It will advise farmers as to their best pathways into both increased profits and increased food yield. And it will allow the worlds rapidly declining cadre of farmers to feed a hungry world in a more sustainable manner.

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Agricultural Artificial Intelligence: Because it's a Hungry World Out There - Electropages

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