The Role Of Artificial Intelligence In Future Technology – The transformative effects of artificial intelligence are coming faster than we recognize. For government, this is a huge opportunity for unprecedented efficiency. However, it also brings a lot of regulatory challenges.

The rise of artificial intelligence (AI) technology has significant implications for state and local governments. One of the main implications is the potential for AI to improve the efficiency and effectiveness of government services. For example, AI-powered chatbots can provide 24/7 customer service for citizens, while machine learning algorithms can analyze large amounts of data to identify patterns and insights that can inform decision-making.

The Role Of Artificial Intelligence In Future Technology

Additionally, AI can be used to automate routine tasks, such as processing paperwork and data entry, freeing government employees to focus on more complex and value-added tasks. However, there are also concerns about the impact of AI on jobs and privacy, and governments will need to consider these issues when implementing AI-based solutions.

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Analysts hail AI as the next wave of technology. So I tried it with the new chatbot, ChatGPT. I asked it to tell us about the implications of AI for government and federalism, and the bot wrote the first two paragraphs above.

The result: not too bad. In fact, it can be difficult to distinguish between the chatbot’s contribution and lead paragraphs from a human writer. These paragraphs could use some good editing. There are missing pieces, like exactly what the “more complex and value-added tasks” might be. But, in general, they are not bad, and they quite neatly encapsulate the promise and the possible pitfalls of this rapidly developing technology, not only in our daily lives but also for government at all levels. And they raise, at least indirectly, the issue of how the technology should be regulated and by what level of government.

The AI ​​opportunities are growing faster than we can keep up with them. For example, Boston Dynamics has developed an egg-powered robotic dog named Spot. It can be alone with complex tasks, such as traversing unfamiliar terrain and figuring out how to open doors, instead of an operator walking it through each step in the process. In dangerous situations, deploying Spot instead of police officers can be a real life-saver.

To demonstrate what its robots can do, Boston Dynamics demonstrated spot for “60 Minutes” and it also produced a video of dancing dogs that got a lot of views. The New York City police department was impressed enough with Spot that it leased one of the robots for a test. “The dog is going to save lives, protect people and protect officers, and that’s our goal,” explained Frank DiGiacomo, a technical response unit inspector for the department. It came in handy, for example, in a hostage situation, where the NYPD’s robot, Christened Digidog, was able to take food to the hostages.

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But Digidog caught out New Yorkers who saw the mother on patrol. Critics have complained that the robot is exacerbating racial conflicts and being used as a “robotic surveillance ground drone,” as the U.S. it. Rep. After people’s complaints, New York sent its robot back to the manufacturer early.

In Dallas, a robot – ironically, one intended for bomb disposal – carried an explosive to where a gunman was hiding and blew him up. While the robot is controlled by a human, its use in Dallas nonetheless fueled debate about exactly how artificial intelligence can and should be used in policing. The debates have spilled over detection systems such as police surveillance of license plates, the linking of doorbell cameras to police investigations and the tracking of suspects through cellphone pings. The Transportation Security Administration (TSA) is testing facial recognition at select airports as an advanced system of identity checks, but it is being carried out carefully because TSA officials know the furor the system could trigger.

Not everything government can do with AI invites such virulent comments. In customer service, for example, the public sector can follow the lead of Amazon and Domino’s Pizza, which already provide rich online, AI-driven assistance. AI can speed up document scanning and processing. It can predict service needs, from the pace of fire and EMT calls to longer-term housing crises, and it can help public employees navigate HR benefits. By analyzing vast quantities of data, it can identify the patterns in public health crises. This has already proven invaluable as “a pandemic crystal ball” in predicting the spread of new COVID-19 variants.

The promises for both the private and public sectors seem endless. Britain’s Chatham House think tank calls AI “a technology that is likely to be as transformative to human history as the Industrial Revolution.” Deloitte predicts that by 2024 75 percent of governments will be launching at least three enterprise AI efforts.

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But as illustrated by New York’s experiment with Digidog, the spread of AI poses big challenges. One of the great benefits of AI, for example, is that it can learn quickly, but the algorithms that produce the learning are often hidden from users, so people can worry that the machine is not treating them fairly. And what about privacy in the use of the vast amounts of data collected? My iPhone knows that most Saturdays, I go to the grocery store, and it knows which grocery store I visit. When I go to Whole Foods, it remembers what I bought. I’m not worried about what Whole Foods might do with my grocery list, but I can understand why many people are afraid of the idea of ​​the government tracking their habits and movements.

And it’s not an unreasonable concern. Government is lagging behind the private sector in developing AI, so at least currently much of the government’s use of the technology is through these companies, which means that the information can end up in private hands and that regulating its use will likely fall behind. .

There is a dilemma baked into the technology here. People are demanding regulations for self-driving taxis, and they are waking up to the racial inequality that public health algorithms can bring. The software managing waiting lists for kidney transplants, for example, discriminated against African Americans. The problem is not that the algorithms are evil. It is that they rely on data that does not take into account the needs of everyone and that they do not learn quickly enough to correct the underlying problems of inequity and violations of privacy.

The stakes here are high. Estimates of the size of the AI ​​market are wild, and it is predicted to grow to more than $1.5 trillion by 2030. Efforts to mitigate the threats have not kept up, McKinsey reports. From asylum applications to criminal justice, the risks are considerable.

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Not only do we lack a policy for steering AI, but we don’t have a clear sense of what issues most need regulation—or who should do the regulating. The temptation for a “race to the bottom” in regulation is huge to attract a piece of this huge market.

The ethical issues here are also huge, especially since AI systems learn and what they learn won’t be apparent until the systems act. Once they act, it can be very difficult to determine the values ​​that are incorporated into these systems. The more governments deploy AI, the more they risk being complicit in the problem.

Because the scale and the stakes are so great, federal regulation makes the most sense. One alternative, for example, would be to build federal “guardrails” in what private companies do and how they do it. Take autonomous vehicles, for example: federal standards have been elusive, and responsibility has been left to the states. A handful of them, including Arizona, California and Michigan, have taken the lead, but as is always the case, allowing the states to shape policy can result in highly uneven standards that can become captives of large private innovators.

There are currently 21 states that allow autonomous vehicles. Another six govern only autonomous semi-trucks, including the practice of “platooning” – convoys of 18-wheelers bunched closely to each other to cut wind resistance. The law in Arkansas allows trucks to follow 200 feet behind each other, compared to 500 feet in Michigan, Nevada and Wisconsin. What’s legal where is just a taste of the policy confusion coming down the road.

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AI may be the biggest issue that is attracting the least attention. Watching Spot is a gi-viz experience. But debate about the larger issues of safety, privacy, fairness, ethics and trust is lagging behind the incredibly rapid development of AI. At least for now, the resolution of these issues lies in the hands of state and local policymakers.

ChatGPT has its own ideas about regulation. And the bot is right with a poem I asked it to write for us: artificial intelligence has changed the world a lot and will bring many more changes in the coming days. AI is already embedded in our daily lives. Let’s take an example: Many of us use virtual assistants like Google Assistant, Alexa, Siri, etc. to help us. It is predicted that in the near future, AI will be more

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