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Artificial intelligence is only a tool, but what a tool it is. It may be elevating our world into an era of enlightenment and productivity, or plunging us into a dark pit. To help achieve the former, and not the latter, it must be handled with a great deal of care and forethought. This is where technology leaders and practitioners need to step up and help pave the way, encouraging the use of AI to augment and amplify human capabilities.

Those are some of the observations drawn from a recently released report from experts convened by Stanford University and the newest installment out of theOne-Hundred-Year Study on Artificial Intelligence(AI100), an exceptionally long-term effort to track and monitor AI as it progresses over the coming century. The AI100 standing committee is led by Peter Stone, a professor of computer science at The University of Texas at Austin, and executive director of Sony AI America, and the study panel that authored the report was chaired by Michael Littman, professor of computer science at Brown University.

The AI100 authors urge AI be employed as a tool to augment and amplify human skills. "All stakeholders need to be involved in the design of AI assistants to produce a human-AI team that outperforms either alone. Human users must understand the AI system and its limitations to trust and use it appropriately, and AI system designers must understand the context in which the system will be used."

AI has the greatest potential when it augments human capabilities, and this is where it can be most productive, the report's authors argue. "Whether it's finding patterns in chemical interactions that lead to a new drug discovery or helping public defenders identify the most appropriate strategies to pursue, there are many ways in which AI can augment the capabilities of people. An AI system might be better at synthesizing available data and making decisions in well-characterized parts of a problem, while a human may be better at understanding the implications of the data -- say if missing data fields are actually a signal for important, unmeasured information for some subgroup represented in the data -- working with difficult-to-fully quantify objectives, and identifying creative actions beyond what the AI may be programmed to consider."

Complete autonomy "is not the eventual goal for AI systems," the co-authors state. There needs to be "clear lines of communication between human and automated decision makers. At the end of the day, the success of the field will be measured by how it has empowered all people, not by how efficiently machines devalue the very people we are trying to help."

The report examines key areas where AI is developing and making a difference in work and lives:

Discovery:"New developments in interpretable AI and visualization of AI are making it much easier for humans to inspect AI programs more deeply and use them to explicitly organize information in a way that facilitates a human expert putting the pieces together and drawing insights," the report notes.

Decision-making:AI helps summarize data too complex for a person to easily absorb. "Summarization is now being used or actively considered in fields where large amounts of text must be read and analyzed -- whether it is following news media, doing financial research, conducting search engine optimization, or analyzing contracts, patents, or legal documents. Nascent progress in highly realistic (but currently not reliable or accurate) text generation, such as GPT-3, may also make these interactions more natural."

AI as assistant:"We are already starting to see AI programs that can process and translate text from a photograph, allowing travelers to read signage and menus. Improved translation tools will facilitate human interactions across cultures. Projects that once required a person to have highly specialized knowledge or copious amounts of time may become accessible to more people by allowing them to search for task and context-specific expertise."

Language processing:Language processing technology advances have been supported by neural network language models, including ELMo, GPT, mT5, and BERT, that "learn about how words are used in context -- including elements of grammar, meaning, and basic facts about the world -- from sifting through the patterns in naturally occurring text. These models' facility with language is already supporting applications such as machine translation, text classification, speech recognition, writing aids, and chatbots. Future applications could include improving human-AI interactions across diverse languages and situations."

Computer vision and image processing:"Many image-processing approaches use deep learning for recognition, classification, conversion, and other tasks. Training time for image processing has been substantially reduced. Programs running on ImageNet, a massive standardized collection of over 14 million photographs used to train and test visual identification programs, complete their work 100 times faster than just three years ago." The report's authors caution, however, that such technology could be subject to abuse.

Robotics: "The last five years have seen consistent progress in intelligent robotics driven by machine learning, powerful computing and communication capabilities, and increased availability of sophisticated sensor systems. Although these systems are not fully able to take advantage of all the advances in AI, primarily due to the physical constraints of the environments, highly agile and dynamic robotics systems are now available for home and industrial use."

Mobility: "The optimistic predictions from five years ago of rapid progress in fully autonomous driving have failed to materialize. The reasons may be complicated, but the need for exceptional levels of safety in complex physical environments makes the problem more challenging, and more expensive, to solve than had been anticipated. The design of self-driving cars requires integration of a range of technologies including sensor fusion, AI planning and decision-making, vehicle dynamics prediction, on-the-fly rerouting, inter-vehicle communication, and more."

Recommender systems:The AI technologies powering recommender systems have changed considerably in the past five years, the report states. "One shift is the near-universal incorporation of deep neural networks to better predict user responses to recommendations. There has also been increased usage of sophisticated machine-learning techniques for analyzing the content of recommended items, rather than using only metadata and user click or consumption behavior."

The report's authors caution that "the use of ever-more-sophisticated machine-learned models for recommending products, services, and content has raised significant concerns about the issues of fairness, diversity, polarization, and the emergence of filter bubbles, where the recommender system suggests. While these problems require more than just technical solutions, increasing attention is paid to technologies that can at least partly address such issues."

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Artificial intelligence success is tied to ability to augment, not just automate - ZDNet

A pedestrian walks their scooter across Ocean Boulevard on Pine Street in Downtown Long Beach, Thursday, Sept. 23, 2021. Photo by Brandon Richardson.

Long Beachs street congestion and air quality could soon see improvements, thanks to a new pilot program that will test the ability of traffic lights to respond to traffic patterns in real time.

Coined Project X, the collaboration between Mercedes Benz, the city of Long Beach and the Los Angeles-based technology company Xtelligent will deploy a fleet of up to 50 smart vehicles and an artificial intelligence-driven software in the city. The vehicles and software will communicate with each other to provide real-time data to traffic signals.

The project, which will last 10 months, is expected to launch by the end of the year. If successful, the program could move into a second phase once the pilot concludes.

Were expecting intelligent vehicles and connected traffic signals to become industry standard in the next few years, said Ryan Kurtzman, Long Beachs smart cities program manager. Were getting a sneak peek.

The three partners announced that a contract had been signed on Thursday, kicking off the process of selecting a project area and implementing Xtelligents software to test on traffic signals in the selected region.

The cars will mainly be sharing location data, something many cars already do to enable onboard navigation systems. But in this project, they will be sharing this data with city infrastructure, allowing Xtelligents softwareand by extension, city engineersto measure congestion, even calculating emissions based on the type of vehicle and its movements.

The data will be anonymized, preventing anyone in possession of the data to follow any individual cars movements, according to a Mercedes Benz representative.

The potential benefits are manifold, Kurtzman noted.

The implications for traffic flow, for example, are clear. When high congestion is an issue, like around a car crash or during school drop-off and pickup times, customized red and green periods at specific intersections could make traffic flow more smoothly, said Michael Lim, co-founder of Xtelligent.

In the long run, the technology could even allow the city to prioritize carpools or buses, similar to a high occupancy vehicle or bus lane, creating incentives for environmentally-friendly travel, according to Kurtzman.

The system could also improve air quality. In areas that suffer from high pollution, such as major transit and transportation corridors, adaptive traffic signaling could reduce the amount of time cars spend idling at red lights.

If a passenger vehicle is spending less time idling at a red light, thats less time the vehicle is polluting the environment, Kurtzman said. A study of Xtelligents algorithm by the Argonne National Laboratory projected roughly 15% emissions savings as a result of traffic optimization using the companys technology.

Drivers of electric vehicles also stand to benefit from the new technology. Lim, of Xtelligent, drives a Nissan Leaf and said he often struggles with the cars limited range, having to make inconvenient stops just to charge. More efficient traffic signaling can help electric cars like his go farther, he said.

When you have a more predictive, flowing type of movement, theyre able to maintain energy more effectively, Lim said. Having a city infrastructure model that could improve the range of electric vehicles like his, he said, might also encourage more people to make the switch from fossil fuels to electric.

But the first step is launching the pilot program to analyze how well the technology works and what could be improved.

Details of the program, like which streets this particular fleet of intelligent cars will be roaming, are still to be decided. The Atlantic Avenue corridor, parts of Downtown and an area near the Mercedes-Benzs facility near the intersection of the 710 and 405 freeways are among the potential locations.

The city is carefully considering the potential impact of the operation on local traffic and the community overall, Kurtzman said.

We need to make sure that the area makes sense from an engineering standpoint, he said, and from a community standpoint.

The group also plans to start a STEM education program for local students at the Mercedes Benz facility as part of the project, but the details of that program have not yet been released.

If successful, the new technology could have significant benefits for the city, he added.

Systems like that have the potential to improve the efficiency of our transportation network, Kurtzman said. This project helps us inform how we could deploy this type of technology on a larger scale across the city.

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Traffic signal pilot program uses artificial intelligence to ease pollution, congestion in Long Beach - Long Beach Business Journal - Long Beach News

Law Library

AI is becoming more and more prevalent in society, with many people wondering how it will affect the law. How artificial intelligence is impacting our laws and what we can expect for future technology/legal interactions.

The conversation surrounding the relationship between AI and law also touches quite clearly on the ability to rely on Artificial Intelligence to deliver fair decisions and to enhance the legal systems delivery of equity and justice.

In this article, I share insights from my conversations on this topic with Joilson Melo, a Brazilian law expert, and programmer whose devotion to equity and fairness led to a historic change in the Brazilian legal system in 2019, this change mainly affected the system that controls all processes processed digitally in Brazil, the PJe (Electronic Judicial Process).

As a law student, Melo filed a request for action in the National Council of Justice (CNJ) against the Court of Justice of Mato Grosso, resulting in a decision allowing citizens to file applications in court electronically without a lawyer and within the Special Court, observing the value of the case, so that it does not exceed 20 minimum wages. Melos petition revealed provisions in the law that allowed for this and his victory enforced those provisions. The results for the underprivileged and those who couldnt afford lawyers have been immense.

On the relationship between AI and the Law, Melo remains a bit on the fence;

The purpose of the law is justice, equity, and fairness, says Melo.

Any technology that can enhance that is welcome in the legal arena. Artificial Intelligence has already been shown that it can be as biased as the data that it is fed. This instantly places a greater burden of care on us to ensure that it is adopted through a careful process in the legal space and society at large

The use of AI to predict jury verdicts has been around for quite some time now, but it's unclear whether or not an algorithm can accurately predict human behavior. There have also been studies that prove that machine learning algorithms can be used to help judges make sentencing decisions based on factors such as recidivism rates.

In theory, this seems to solve a glaring problem, the algorithm tools are supposed to predict criminal behavior and help judges make decisions based on data-driven recommendations and not their gut.

However, as Melo explains, this also presents some deep concerns for legal experts, AI risk assessment tools run on algorithms that are trained on historical crime data. In countries like America and many other nations, law enforcement has already been accused of targeting certain minorities and this is shown by the high number of these minorities in prisons. If the same data is fed, the AI is going to be just as biased.

Melo continues, Besides, the Algorithms turn correlative insights into causal insights. If the data shows that a particular neighborhood is correlated with high recidivism, it doesnt prove that this neighborhood caused recidivism in any given case. These are things that a Judge should be able to tell from his observations. Anything less is a far cry from justice, unless we figure out a way to cure the data.

As we continue developing smarter technologies, data protection becomes an increasingly important issue. This includes protecting private information from hackers and complying with GDPR standards across all industries that collect personal data about their customers.

Apart from the GDPR, not many countries have passed targeted laws that affect big data. According to the 2018 Technology Survey by the International Legal Technology Association, 100 percent of law firms with 700 or more lawyers use AI tools or are pursuing AI projects.

If this trend continues and meets with the willingness of courts and judges to adopt AI, then they would eventually fall into the category of companies that need to abide by the data protection rules. Client/Attorney privilege could be at risk of a hack and court decisions as well.

The need for stringent local laws that help regulate how data is received and managed has never been more clear, and this is why it is shocking that many governments have not acted faster.

Joilson Melo

Many governments have an unholy alliance with tech giants and the companies that deal most with data, says Melo.

These companies are at the front of national development and are the most attractive national propositions for investments. Leaders do not want to stifle them or be seen as impeding technological advancement. However, if the law must apply equally, governments should take a cue from the GDPR and start now before we see privacy violation worse than we already have.

As Artificial Intelligence becomes more ingrained in our lives, so do the legal issues that surround it.

One of the most prevalent legal questions is whether machines should be allowed to possess self-driving cars and deadly weapons. Self-driving cars are already on the market but they have a long way to go before they could replace human drivers. The technology has not been perfected yet and will require huge strides forward before we can say with certainty that these vehicles are safe for society at large.

The larger concerns about these touch on how easily these algorithms can be hacked and influenced externally.

AI and Weapons/War Crimes: The possibility of autonomous weapons systems has been touted in many spheres as a powerful way to identify and eliminate threats. This has come against strong pushback for obvious reasons. Empathy, concession, and a certain big-picture approach have always played crucial roles in war and border security. These are traits that we still cannot inculcate into an algorithm.

Human Rights Questions: One of the main questions that arise in the area of human rights is with regards to algorithmic transparency. There have been reports of people losing jobs, being denied loans, and being put on no-fly zones with no explanation other than, it was an algorithmic determination.

If this pattern persists the risk to human rights is enormous. The questions of cybersecurity vulnerabilities, AI bias, and lack of contestability are also concerns that touch on human rights.

Melos concern seems more targetted at the law and how it can be preserved as an arbiter of justice and enforcer of human rights and he rightly points out the implications of leaving these questions unanswered;

Deciding not to adopt AI in society and legal systems is deciding not to move forward as a civilization, Melo comments.

However, deciding to adopt AI blindly would see us move back into a barbaric civilization.I believe that the best approach is to take a piece-meal approach towards adoption; take a step, spot the problems, eliminate them and then take another step.

The law and legal practitioners stand to gain a lot from a proper adoption of AI into the legal system. Legal research is one area that AI has already begun to help out with. AI can streamline the thousands of results an internet or directory search would otherwise provide, offerring a smaller digestible handful of relevant authorities for legal research. This is already proving helpful and with more targeted machine learning it would only get better.

The possible benefits go on; automated drafts of documents and contracts, document review, and contract analysis are some of those considered imminent.

Many have even considered the possibilities of AI in helping with more administrative functions like the appointment of officers and staff, administration of staff, and making the citizens aware of their legal rights.

A future without AI seems bleak and laborious for most industries including the legal and while we must march on, we must be cautious about our strategies for adoption. This point is better put in the words of Joilson Melo; The possibilities are endless, but the burden of care is very heavy we must act and evolve with cautiously.

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Justice, Equity, And Fairness: Exploring The Tense Relationship Between Artificial Intelligence And The Law With Joilson Melo - Forbes

Investors have realized that major disruptive technologies such as AI have a high chance to thrive in the tech-driven future. Tech companies are focused on creating and manufacturing new innovations with artificial intelligence and machine learning algorithms to raise the standard of living in the global society. Thus, the demand for artificial intelligence stocks is also rising at an increasing rate. Investment in AI stocks can help to gain higher revenue instead of a massive loss because the tech stock market is not highly volatile like the cryptocurrency market. There are some ups and downs in the artificial intelligence stocks due to the impact on the demand for the COVID-19 pandemic. Some of these stocks have the potential to rise in the future despite experiencing a dip. Lets explore the top 5 AI stocks at the dip, which could rise to big heights in the future.

Splunk

Spunk is one of the tech companies that provide solutions to ensure success in the digital needs of clients. The flexible platform and purpose-built solutions scale with clients as the data and company evolves. Splunk has experienced a dip of -5.8% in its artificial intelligence stock in 2021 due to the ongoing pandemic. But the tech company is expecting a bounce in revenue in the upcoming months owing to the change in the situation. The AI stock at dip showed a downtrend for over six months but it has been in an uptrend since June 2021. Investment in AI stock is lucrative now because the current price of this artificial intelligence stock is US$149.89 with a market cap of US$24.21 billion.

Teladoc Health

Teladoc Health is known as the worlds only integrated virtual care system for delivering and empowering whole-person health. The tech company experienced a dip at the beginning of 2021 and the AI stock showed a downward trend with over 24% in February despite having positive revenue in the fourth quarter of 2020. Investors are expecting positive growth in this artificial intelligence stock with a good performance from the tech company. Teledoc Health expects to reach US$265 million with adjustments in earnings through interests and taxes. The market cap, at the beginning of 2021, was US$42 million but now it is US$22.08 billion with a current price of US$138.67.

Verastem Inc.

Verastem Inc. is known as a biopharmaceutical company that engages in the development and commercialization of drugs to cure cancer. The AI stock at dip was presented due to its capital-raising efforts. Investors are expecting a rise in one of the top artificial intelligence stocks in 2021 because the current price is US$2.99 with a market cap of US$540.47 million. Recently, the investment in the AI stock is lucrative now because the company experienced positive growth owing to its Phase FRAME study in VS-6766 for low-grade serous ovarian cancer.

Twilio Inc.

Twilio has experienced a sharp dip with a plunge ranging from 5.6% to 4.7%. The second quarter showed positive growth in revenue of US$668.90 million with an adjusted loss per share of US$0.11 despite having expectations of yielding US$598.37 million as revenue with a loss per share of US$0.13. Twilio is expanding its customer base and participating in acquisitions with top companies in the tech-driven market. The growing ecosystem of cloud-based communications tools is attracting the eyes of investors in 2021 towards the artificial intelligence stock. Twilio is one of the popular tech companies that provides a cloud-based communication platform to allow developers to operate customer engagement within the software applications across the world. The investment in AI stock is lucrative now because of the current artificial intelligence stock price of US$349 and a market cap of US$61.82 billion.

Pinterest, Inc.

Pinterest is a popular tech company that experienced an AI stock dip recently. The companys stocks have fallen to 25% in value since July 2021. The dip is anticipated to be a temporary setback for investors with a loss of 24 million users from the previous quarters. There is still a lot of revenue growth to be earned despite having a second-quarter ARPU at an 89% increase. Investors and analysts expect a rise in revenue of 53% to US$2.6 billion in 2021 with a current price of US$54.18 with a market cap of US$34.93 billion.

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Investment Alert: Top 5 Artificial Intelligence Stocks to Buy at the Dip - Analytics Insight

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