Opinions on Artificial Intelligence Vary in Finland – Fagen wasanni

A recent survey conducted by the independent non-profit organization Foundation for Municipal Development revealed different perspectives among the Finnish population regarding the benefits and risks associated with artificial intelligence (AI).

The survey, which involved over 1,000 participants, found that 62% of respondents believed AI would enhance industrial production efficiency, while 50% thought it would increase work productivity. However, almost half of the participants expressed concerns about AI weakening privacy protection, and over a third believed it would have a negative impact on job opportunities and customer service. Furthermore, around a third of the respondents felt that accessing accurate, error-free information would become more difficult with the adoption of AI.

Regarding transportation safety, approximately 40% of those surveyed believed that AI would improve it, while others were unsure or believed it would have no significant effect. The opinions on the impact of AI on climate change, democracy, and social equality were also divided.

The survey participants had diverse views on the personal impact of AI in their lives. Around a fifth anticipated a positive impact, a similar number expected negative consequences, and the remainder were uncertain.

Political affiliation was found to shape perceptions of AI. Supporters of the National Coalition Party and the Greens were more likely to hold positive opinions about the technology, while those backing the Finns Party and the Centre Party expressed more negative views. Age was another influencing factor, as younger people tended to view AI more positively, while older individuals, rural residents, and those with lower education levels were more pessimistic.

The survey conducted by Kantar Public took place in June.

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Opinions on Artificial Intelligence Vary in Finland - Fagen wasanni

The Impact of Artificial Intelligence on Society – Fagen wasanni

This summer, artificial intelligence (AI) demonstrated its remarkable capability by extracting John Lennons voice from a demo song recorded shortly before his death in 1980. By removing the electrical buzzing and piano accompaniment, AI successfully mixed Lennons voice into a final Beatles project led by Paul McCartney.

The ability of AI to recognize distinctive human voices has captivated the attention of many. However, it has also raised concerns about the potential impact of this powerful tool. Like any tool, the impact of AI depends on the intentions of the user. While it has many beneficial uses in our daily lives, such as grammar autocorrect and real-time navigation on smartphones, there is also the possibility of AI being manipulated for malicious purposes.

Instances of AI impersonating individuals for nefarious reasons have already occurred. For example, a mother in Arizona received a convincing AI-engineered recording of her daughter screaming that she had been kidnapped. The perpetrator threatened to harm the girl if a ransom was not paid. Fortunately, it was later discovered that the girl was safe at a skiing competition, but this incident highlights the potential dangers of AI.

These contrasting stories of AIs applications underscore the need for responsible use and regulation of this technology. While international gatekeepers work towards encouraging responsible AI utilization and preventing its abuses, it is essential for individuals to understand the implications and impact of AI in their daily lives.

Taking the time to understand ourselves and others on a deeper level through traditional means is crucial. A chance encounter between strangers, as witnessed during a family reunion, demonstrated how people from different backgrounds and worlds can connect through simple gestures. Moreover, taking the time to pay attention to nonverbal cues and support those with special needs, like the authors son, fosters true understanding and communication.

Additionally, AI can assist in organizing and finding relevant photos, as demonstrated by face recognition technology. However, there will always be a significant difference between recognizing someones face and cherishing the connection and memories associated with that individual.

In conclusion, while AI has undoubtedly shown its potential for innovation and discovery, it is crucial to exercise caution and responsible usage to prevent any negative consequences. Balancing the benefits of AI with human connection and understanding is key to ensuring a harmonious coexistence with this technology.

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The Impact of Artificial Intelligence on Society - Fagen wasanni

Artificial Intelligence and the Perception of Dogs’ Ears – Fagen wasanni

The use of generative artificial intelligence in the world of art has sparked mixed reactions. Photographer Sophie Gamand recently explored how AI views dogs ears in her project featuring shelter dogs with cropped ears. Surprisingly, the AI algorithms leaned towards the belief that dogs should have floppy ears, despite the existence of breed standards and human preferences for cropped ears.

Using her own photographs of shelter dogs, many of which had severely shortened ears, Gamand aimed to restore their ears through AI technology. She utilized the DALL-E 2 program to understand how AI perceives a dogs appearance. Although the process was occasionally frustrating, Gamand wanted to minimize her interference to truly explore what the computer thought a dog should look like. It turned out that AI considers dogs to have intact ears.

Gamand believes that AI has the potential to separate genuine artists from those who rely too heavily on the technology. While AI can create stunning images, it is crucial for artists to consider their own artistic context, aesthetics, and the messages they want to convey. The use of AI should align with an artists overall vision and not solely rely on the work of others.

The ear cropping project is just one example of Gamand using AI in her work. She has also transformed AI interpretations of dogs into oil paintings and used ChatGTP to craft a letter from a shelter dog to its previous owner. Despite the benefits of AI, Gamand emphasizes the importance of ethical and honest artistic practices with this technology.

Gamands photography focuses on raising awareness for misunderstood dog breeds and animals in shelters. She has dedicated her time to volunteering at shelters across the United States and has successfully fundraised for animal shelters through her Instagram feed. Gamand believes that photographs have the power to create emotional connections between adoptable animals and potential pet owners.

Through her artwork, Gamand aims to reflect on humanity by observing dogs. However, sometimes the mirror reveals uncomfortable truths, such as the prevalence of ear cropping. She questions why certain breeds continue to undergo this procedure for aesthetic reasons, even though they are living safely as family pets. Gamand believes this reflects a broader issue in our relationship with dogs and the natural world, highlighting the need for better understanding and decision-making on behalf of our companions.

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Artificial Intelligence and the Perception of Dogs' Ears - Fagen wasanni

The Elements of AI: Free Online Course on Artificial Intelligence – Fagen wasanni

The field of artificial intelligence (AI) has revolutionized various aspects of our lives, enabling machines to perform tasks that were previously exclusive to human intelligence. However, along with the countless opportunities that this technological revolution has brought, there are also ethical, security, and regulatory challenges to navigate. To address this pressing need, an online initiative called Elements of AI has been created.

Elements of AI is a collaboration between Reaktor Inc. and the University of Helsinki, and it offers an online course that provides a solid foundation for understanding AI. The course is presented online and free of charge, making it accessible to anyone interested in delving into the fascinating world of AI.

The course is divided into two parts. The first section, Introduction to AI, introduces participants to the core concepts of AI. This module is designed for beginners who have no prior knowledge of AI. The second section, Creating AI, is aimed at individuals with basic programming skills in Python. In this phase of the course, participants explore how to build practical AI applications and delve into the capabilities of this disruptive technology.

Upon completing the course, participants receive an Artificial Intelligence certification, which not only enriches their knowledge but also adds professional credibility. In a competitive and rapidly evolving job market, this certification serves as a mark of quality and competence.

Since its launch in May 2018, Elements of AI has had over 140,000 subscriptions from more than 90 countries worldwide. The vision behind this course is to inspire, educate, and promote well-being through knowledge. It has been praised by Sundar Pichai, CEO of Google, as an inspiring example that levels the playing field and allows more people to benefit from the advances of AI.

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The Elements of AI: Free Online Course on Artificial Intelligence - Fagen wasanni

Jeremy Taylor: Use artificial intelligence to protect mobile devices … – Rocky Mount Telegram

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Jeremy Taylor: Use artificial intelligence to protect mobile devices ... - Rocky Mount Telegram

Artificial Intelligence in Real Estate: The Rise of Bot Agents – Fagen wasanni

Artificial intelligence (AI) algorithms are now capable of accurately predicting the price of a house by simply analyzing visual data, such as Google Street View images. However, while this technology offers great potential, it also raises concerns about its impact on the property market.

Visual inspections play a vital role in real estate. Agents gather data on a propertys layout, comparable prices, and neighborhood amenities, but they also rely on in-person visits to make accurate assessments. Skilled professionals can observe subtle details such as potholes, storefronts, car models, and the composition of crowds, all of which provide valuable insights into a propertys value. This street-level assessment is particularly important in identifying up-and-coming neighborhoods before prices reflect their popularity.

Visual AI now has the ability to replicate this street-level analysis on a larger scale. Researchers at MITs Senseable City Lab trained an AI model using 20,000 pictures of homes in Boston and data on how their prices changed over time. Their deep learning algorithm identified correlations between visual features of homes and changes in their values. By incorporating additional variables like structural information and neighborhood amenities, the algorithm accurately predicted how prices would evolve over time.

The potential applications of visual AI extend beyond predicting property values. As demonstrated in a recent study, analyzing 27 million street view images across the US enabled researchers to predict various aspects of a neighborhoods profile, including poverty levels, crime rates, and public health indicators. The next step in this advancement could involve using publicly-accessible photos from real estate websites and social media to assess the interior of homes, identifying features like renovated bathrooms or upscale kitchens.

While these technologies, combined with broader economic indicators like mortgage rates, could become powerful tools for the real estate industry, they also pose certain risks. Algorithms may perpetuate biases, such as undervaluing properties belonging to racial minorities. Furthermore, relying on AI predictions could create self-fulfilling prophecies, as individuals may optimize their homes to impress algorithms rather than meet personal preferences or needs.

To navigate these challenges, a balance of regulation and experimentation is necessary. Increasing the number of AI models in use can prevent undue influence from a single imperfect algorithm. However, it will still be up to human judgment to interpret the insights provided by these new visual AI technologies. While AI can predict much about the world, reimagining a better future remains a uniquely human endeavor.

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Artificial Intelligence in Real Estate: The Rise of Bot Agents - Fagen wasanni

Regulating Artificial Intelligence in the National Health System – Fagen wasanni

An initiative has been presented to establish a regulatory framework for the safe, effective, and ethical use of artificial intelligence (AI) in the National Health System. This initiative aims to improve patient safety, guarantee adequate treatment, and protect sensitive personal data.

Companies in Puebla are already applying AI in the field of health. Elon Musk has launched XAI, an AI company focused on reliability, accuracy, privacy, safety, quality, and medical efficacy. The use of AI in both physical and digital media is intended to enhance patient safety and ensure proper treatment.

The proposed reform includes measures to protect and treat sensitive personal data in the development and use of AI systems in the health sector. It also emphasizes the protection and adequate treatment of sensitive personal data within the objectives of the national health system.

The Federal Commission for Protection against Sanitary Risks will assess health risks related to the use of AI systems in the field of health. Additionally, the management of information, including sensitive personal data, must be protected when studying the human genome.

The fifth part of the initiative focuses on AI in health, defining it as systems based on digital algorithms that mimic human intelligence. These systems can autonomously or assistively perform cognitive tasks in the field of health.

It is important to note that AI should not be considered a replacement for health professionals. Instead, it should be regarded as a support tool, with final decision-making entrusted to trained human professionals.

The Secretariat will promote adequate training and education on the ethical and safe use of AI, as well as the training of human resources to facilitate its adoption in the national health system. Furthermore, the protection of sensitive personal data during all stages of research and medical use is also emphasized.

Overall, the proposed initiative seeks to regulate the use of AI in the National Health System, ensuring its safe and ethical application while protecting sensitive personal data and improving patient safety and treatment.

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Regulating Artificial Intelligence in the National Health System - Fagen wasanni

Artificial Intelligence Used to Create Image of Former CM in … – Fagen wasanni

As the popular flower show at Lal Bagh botanical gardens in Bangalore kicks off, a multimedia company called Maya Films is utilizing Artificial Intelligence (AI) to create images. This year, the flower show pays tribute to Kengal Hanumanthaiah, a former Chief Minister of Karnataka. Maya Films will use AI to generate an image of the late CM taking a stroll in Lal Bagh.

Kengal Hanumanthaiah served as Karnatakas second chief minister from 1952 to 1956 and was known for his involvement in the construction of Vidhana Soudha. During his free time, Hanumanthaiah enjoyed walking inside Lal Bagh, but there is no recorded image of him inside the park. In honor of his contributions, Maya Films decided to recreate the scene using AI.

The theme of this years flower show is the Vidhana Soudha, which is the seat of the state legislature in Karnataka. To complement the tribute to Hanumanthaiah, a replica of Vidhana Soudha has been erected with flowers next to his statue inside Lal Bagh. The flower show is conducted twice a year, and Karnataka Chief Minister Siddaramaiah inaugurated the event.

The use of AI in creating the image of the former CM showcases the potential of technology in the field of multimedia. By harnessing AI, Maya Films aims to give visitors the opportunity to witness history and experience the presence of Hanumanthaiah in Lal Bagh during the 1950s. This innovative project serves as a testament to how AI can be utilized in creative ways to enhance our understanding of the past.

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Artificial Intelligence Chatbots are Known to Spout Falsehoods – Fagen wasanni

Artificial intelligence chatbots, including OpenAIs ChatGPT and Anthropics Claude 2, have been found to produce false information, leading to concerns among businesses, organizations, and students using these systems. The issue of generating inaccurate information, described as hallucination or confabulation, poses challenges for tasks that require reliable document composition and work completion. Developers of large language models, such as ChatGPT-maker OpenAI and Anthropic, acknowledge the problem and are actively working to improve the truthfulness of their AI systems.

However, experts question whether these models will ever reach a level of accuracy that would allow them to safely provide medical advice or perform other critical tasks. Linguistics professor Emily Bender suggests that the mismatch between the technology and its proposed use cases makes it inherently unfixable. The reliability of generative AI technology is crucial, as it is projected to contribute trillions of dollars to the global economy.

The use of generative AI extends beyond chatbots and includes technology that can generate images, videos, music, and computer code. Accuracy is particularly important in applications like news-writing AI products and recipe generation. For example, a single hallucinated ingredient in a recipe could lead to an inedible meal. Partnerships between AI developers like OpenAI and news organizations like the Associated Press highlight the significance of accurate language generation.

While the CEO of OpenAI, Sam Altman, expresses optimism about addressing the hallucination problem, experts like Emily Bender believe that improvements in language models wont be sufficient. Language models are designed to model the likelihood of different word strings, making them adept at mimicking writing styles but prone to errors and failure modes.

Despite potential accuracy issues, marketing firms find value in chatbots that produce creative ideas and unique perspectives. The Texas-based startup Jasper AI collaborates with OpenAI, Anthropic, Google, and Meta (formerly Facebook) to offer AI language models tailored to clients specific requirements, including accuracy and security concerns.

Addressing the challenges of hallucination and improving the reliability of AI chatbots and language models will contribute to their widespread and trustworthy use for various applications.

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Artificial Intelligence Chatbots are Known to Spout Falsehoods - Fagen wasanni

The Role of Big Data and Artificial Intelligence in Asia Pacific … – Fagen wasanni

Exploring the Impact of Big Data and Artificial Intelligence on Asia Pacific Hospital Information Systems

The role of Big Data and Artificial Intelligence (AI) in Asia Pacific Hospital Information Systems is rapidly evolving, transforming the healthcare landscape in unprecedented ways. This shift is driven by the need to improve patient care, streamline operations, and enhance decision-making processes in healthcare institutions.

Big Data, a term that refers to the vast amount of data generated every second, is being harnessed by hospitals to gain insights into patient health, disease patterns, and treatment outcomes. This data, which can range from patient records to real-time monitoring of vital signs, is analyzed to identify trends, predict outcomes, and inform treatment plans. For instance, in Singapore, the use of Big Data in healthcare has led to the development of predictive models that can forecast disease outbreaks, enabling authorities to take proactive measures.

Artificial Intelligence, on the other hand, is being used to automate routine tasks, analyze complex medical data, and even assist in diagnosis and treatment. In Japan, AI is being integrated into hospital information systems to help doctors interpret medical images, reducing the time taken to diagnose conditions such as cancer. Similarly, in China, AI algorithms are being used to analyze electronic health records to predict patient readmission rates, helping hospitals to manage resources more effectively.

The integration of Big Data and AI into hospital information systems is not without challenges. Data privacy and security are major concerns, especially given the sensitive nature of health information. Hospitals must ensure that they have robust systems in place to protect patient data from breaches and misuse. Additionally, the lack of standardized data formats can hinder the effective use of Big Data, while the complexity of medical data can pose challenges for AI algorithms.

Despite these challenges, the potential benefits of Big Data and AI in healthcare are immense. They can lead to more accurate diagnoses, personalized treatment plans, and improved patient outcomes. Moreover, they can help healthcare providers to identify inefficiencies, reduce costs, and improve the quality of care.

In the Asia Pacific region, governments are recognizing the potential of Big Data and AI in healthcare and are taking steps to foster their adoption. For example, the Australian government has launched a national strategy to harness the power of AI in healthcare, while the Indian government has initiated a program to promote the use of Big Data in public health.

The role of Big Data and AI in Asia Pacific Hospital Information Systems is set to grow in the coming years. As technology advances and more data becomes available, these tools will become increasingly integral to healthcare delivery. However, it is crucial that hospitals navigate the challenges associated with their use and ensure that they are used ethically and responsibly.

In conclusion, the impact of Big Data and AI on Asia Pacific Hospital Information Systems is profound, offering opportunities to revolutionize healthcare delivery. By harnessing these technologies, hospitals can improve patient care, streamline operations, and make more informed decisions. However, to fully realize these benefits, hospitals must address the challenges associated with their use and ensure that they are used in a way that respects patient privacy and promotes trust.

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The Role of Big Data and Artificial Intelligence in Asia Pacific ... - Fagen wasanni

nGrow.ai: Revolutionizing Business Operations with Artificial … – Fagen wasanni

nGrow.ai is an artificial intelligence (AI) platform that is transforming the way companies optimize their business operations. With its wide range of features and functions, nGrow.ai automates and streamlines tasks and processes, increasing overall efficiency and productivity.

The platform offers various use cases and features that can be customized to meet the specific needs of each company. For example, e-commerce companies can automate inventory management to reduce errors and ensure sufficient stock levels. Customer service can be improved by automating responses to common inquiries, reducing wait times and enhancing customer satisfaction.

nGrow.ai also provides the capability to create custom dashboards that offer real-time insights into business operations. These dashboards display key metrics such as employee performance, project status, and sales, enabling managers to make informed decisions based on up-to-date data. AI algorithms generate actionable insights to help identify growth opportunities and strategies to improve operational efficiency.

The main advantage of using nGrow.ai is the ability to automate and optimize operations. Advanced AI algorithms handle repetitive and tedious tasks quickly and accurately, freeing up employees to focus on higher-value activities. This automation saves time and reduces human error, ultimately improving efficiency and saving costs.

In addition to automation, nGrow.ai offers tools to optimize existing operations by analyzing workflows and identifying areas for improvement. Detailed analytics provide a comprehensive view of how operations are performing, empowering companies to make informed adjustments and maximize efficiency.

One standout feature of nGrow.ai is the creation of custom dashboards, which provide real-time insights tailored to each businesss specific needs. Managers can track daily sales, revenue, and individual team member performance, enabling them to make quick decisions to improve sales and optimize performance.

The platforms AI-powered insights are also instrumental in identifying growth opportunities. By analyzing data and spotting hidden patterns and trends, nGrow.ai helps companies tap into new markets, customer segments, or products that may have been overlooked.

nGrow.ai not only saves time and money but also provides comprehensive analytics and reports to identify areas for improvement. By taking corrective action based on this information, companies can streamline workflows, reduce downtime, and errors.

While nGrow.ai offers numerous advantages, there are a few drawbacks to consider. The platform may require a learning curve to fully utilize all its features, and it might be expensive for small businesses with limited budgets.

In conclusion, nGrow.ai is an AI platform that revolutionizes business operations. With features such as custom dashboard creation, AI-powered insights, and in-depth analytics, it offers a comprehensive solution to improve efficiency and performance. By leveraging nGrow.ai, companies can save time and money, identify growth opportunities, and maximize operational efficiencies.

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nGrow.ai: Revolutionizing Business Operations with Artificial ... - Fagen wasanni

Driving Forces Behind the Expansion of Artificial Intelligence (AI) in … – Fagen wasanni

The Artificial Intelligence (AI) in Fintech Market is experiencing significant growth due to various driving forces. Technological breakthroughs have revolutionized the sector, making it possible to create new goods and services. Alongside this, changing consumer preferences and increased consumer awareness of AI in Fintech have driven demand. Supportive policies and favorable government laws have also encouraged industry growth and investment.

Furthermore, the sector has benefited from access to new markets and clientele through smart alliances and partnerships. These driving forces are working together to propel the Artificial Intelligence (AI) in Fintech Market to new heights, with a positive outlook for continued expansion in the coming years.

The global AI in Fintech Market is expected to experience steady growth in the coming years. This growth will be driven by continuous technological advancements, growing environmental awareness, and the rising need for streamlined operations. To seize the market opportunities, industry players are anticipated to focus on product innovation, strategic collaborations, and geographical expansion.

The market report includes profiles of leading companies operating in the AI in Fintech Market, such as Autodesk, IBM, Microsoft, Oracle, SAP, and Fanuc, among others. The report reveals key market methods that can assist businesses in leveraging their position in the market and diversifying their product range.

The report provides valuable insights into market growth based on in-depth primary and secondary data collection. It also categorizes the AI in Fintech Market based on type, including hardware, software, and services, and application, such as customer service, credit scores, insurance support, and financial market prediction.

The segmentation of the market allows for a more targeted analysis of specific market segments, helping businesses make informed decisions and tailor their strategies accordingly. With comprehensive market insights, in-depth industry analysis, accurate market sizing and forecasting data, and a focus on emerging trends and innovations, this report provides businesses with valuable foresight and a competitive edge in the AI in Fintech Market.

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Driving Forces Behind the Expansion of Artificial Intelligence (AI) in ... - Fagen wasanni

Trump & the KKK Act: Carol Anderson on Reconstruction-Era Voting Rights Law Cited in Trump Indictment – Democracy Now!

This is a rush transcript. Copy may not be in its final form.

AMY GOODMAN: Former President Donald Trump has pleaded not guilty to four felony charges over his efforts to overturn the 2020 presidential election. Trump entered the plea Thursday in the same federal district court in Washington, D.C., where more than a thousand of his supporters have faced criminal charges over the January 6, 2021, attack on the Capitol.

Prosecutors led by special counsel Jack Smith requested a speedy trial, while Trumps legal team asked the magistrate judge for more time to review documents and evidence in the case. Its part of Trumps legal strategy to delay the criminal cases against him until after the 2024 election, which he hopes hell win and then could pardon himself. Trumps first pretrial hearing is set for August 28th.

Trump spoke after the arraignment.

DONALD TRUMP: When you look at whats happening, this is a persecution of a political opponent. This was never supposed to happen in America. This is the persecution of the person thats leading by very, very substantial numbers in the Republican primary and leading Biden by a lot. So, if you cant beat him, you persecute him or you prosecute him. We cant let this happen in America.

AMY GOODMAN: Going forward, the legal proceedings in this case will be presided over by U.S. District Judge Tanya Chutkan, an Obama appointee who has issued some of the toughest sentences for the January 6 rioters, often going beyond what the prosecutors asked for. Judge Chutkan is Black, as are many of those now prosecuting Trump Manhattan DA Alvin Bragg, New York Attorney General Letitia James, Fulton County DA Fani Willis. Theyve all received racist threats.

Meanwhile, the Fulton County Sheriff Patrick Labat, whos also Black, said Tuesday the former president would not receive any special treatment if Trump is indicted in Georgia, where hes being investigated for election interference. Labat said, quote, It doesnt matter your status, we have mugshots ready for you.

A key part of the election interference charges Trump faces relate to a Civil War-era rights law that protects the right of citizens to have their votes counted.

For more, we go to Atlanta, where were joined by Carol Anderson, professor at Emory University, author of many books, including One Person, No Vote: How Voter Suppression Is Destroying Our Democracy.

Professor, welcome back to Democracy Now! Its great to have you with us. First of all, why dont you just respond to the overall indictment and President Trumps appearance yesterday in the Washington, D.C., court, pleading not guilty?

CAROL ANDERSON: The indictment was a long time coming, and it reaffirmed the belief in the rule of law, which it looked like for so long that he would be able to once again skate through, escape the consequences, being held accountable, for his assault on American democracy. And so, seeing him there, watching the sketches as they were coming through, listening to the journalists talking about what was happening in that courtroom, it was like, Finally, finally, finally.

AMY GOODMAN: And so, talk about what legal analysts are now describing as a very elegant, streamlined series of charges, only four. They dont, by the way, include seditious conspiracy or insurrection. Talk about the significance of each one.

CAROL ANDERSON: So, what Jack Smith has laid out is the conspiracy to defraud the U.S. government, the conspiracy to basically subvert a political legal process for the United States. And the one that really attracts me is the conspiracy against rights, which is the right to vote, because underlying the Big Lie was the big lie of voter fraud. And that big lie of voter fraud was targeted at communities, at cities that have sizable Black and minority populations, and it was trying to delegitimize the votes of those American citizens.

And so, this is so streamlined because there are six in that indictment, there are six unindicted co-conspirators, but theyre not on the charge itself. It is the United States of America v. Donald J. Trump. And so, thats to make sure that this thing is clean, its smooth. There are none of these pieces like we have with Mar-a-Lago with multiple defendants, with classified documents, that this thing can go through. So, the defenses claims of were having an inordinate amount of discovery that we have to go through, of the documents and the witness testimonies that the prosecutor has amassed, so much of that they already have from the January 6th committee hearings. Whats new, for instance, is Mike Pence, who went before the grand jury and told about his conversations with Trump.

AMY GOODMAN: I want to talk about Georgia, where you are. Youre a professor at Emory University in Atlanta. It was mentioned something like 48 times. Now, Im talking about this federal indictment, not whats happening right now. I mean, a grand jury is meeting today once again in Atlanta, and those charges might come down anytime from the DA, Fani Willis. But Georgia being mentioned 48 times in the federal indictment, and then, of course, Michigan mentioned scores of times, as well. Talk about the significance of what happened in Georgia and how that relates to the federal issue.

CAROL ANDERSON: Yes. So, Georgia was targeted targeted hot, heavy and hard by the Trump regime. So, you have that infamous phone call from Trump to Brad Raffensperger, who was the secretary of state, where Trump is saying, All I need is 11,780 votes. Just find me 11,000 votes, and Raffensperger pushing back, saying, The data dont support that. We dont have those numbers. And Trump is just demanding that Raffensperger overturn the will of the voters here in Georgia and just conjure up some votes and plug a number in there that says that Trump won the 16 Electoral College votes out of Georgia.

When that didnt work, they also had the fake elector scheme, where you have the legal electors are already meeting in the statehouse, as the law requires. Then, the fake electors then sneak into the statehouse on December 14th, and theyre meeting there, and they actually sign a document that says that they are the electors from the state of Georgia and that they then cast their 16 Electoral College votes for Donald J. Trump. And then they send that document to the federal judge, to the president of the Senate and to the head of the National Archives, giving the aura that this is legitimate, when it is actually illegitimate.

And then you have Mark Meadows coming into Georgia at a counting center as a recount is happening over absentee ballots. I mean, hard, hot and heavy pressure on Georgia to overturn the will of the voters.

And let me be really clear about the will of the voters. Ninety percent of Black voters in Georgia voted for Joseph Biden. Almost 70% of Hispanic voters in Georgia voted for Joseph Biden. And more than 60% of Asian American voters in Georgia voted for Joseph Biden. So this attempt to wipe out those votes is wiping out the votes of sizable blocs of minority voters, who did not vote for Donald J. Trump.

AMY GOODMAN: I want to talk about the issue of violence, because Donald Trumps defenders are continually saying Im thinking of people like Kevin McCarthy right? the House speaker saying, Hes just being accused of thought crimes, things he thought or said, and anyone can say or think things.

But this is The Atlantic journalist Adam Serwer, who was pointing out on social media, The indictment makes clear that Donald Trump and his accomplices planned to seize power by force and then maintain that power through the mass murder of American citizens by their own military.

The indictment says this: Also on January 4, when Co-Conspirator 2 acknowledged to the Defendants Senior Advisor that no court would support his proposal, the Senior Advisor told Co-Conspirator 2, '[Y]ou're going to cause riots in the streets. Co-Conspirator 2 responded that there had previously been points in the nations history where violence was necessary to protect the republic.

If you could respond to that, Professor Anderson, and also the significance, of course, of Mark Meadows, the chief of staff, who you just mentioned, who might well have flipped right now and be working with Jack Smith?

CAROL ANDERSON: Absolutely. So, you have not only Eastman, but you also have Jeffrey Clark of the Department of Justice being warned that this attempt to override the election, overturn the will of the voters, would lead to folks being out in the streets, would lead to riots. And the response was, Well, thats what the Insurrection Act is for. So, there was a willingness to use the U.S. military against American citizens who were protesting for their rights, protesting, fighting for this democracy, protesting because the will of the voters had been overturned by a cabal of co-conspirators, a cabal who were in league with Donald J. Trump. And so, that willingness to use violence to overturn democracy is it just tells you how deeply embedded this drive was to keep him in power, and the disregard they had for the lives of American citizens, who withstood a pandemic, a deadly pandemic, to go and vote, who understood that democracy was on the line and were willing to do what they needed to do.

So, in terms of violence, I also have to talk about Rudy Giuliani coming down here to Georgia for three legislative hearings, where he spews he and his team spew a bevy of lies about dead people voting, but particularly about Shaye Moss and Ruby Freeman, two Black poll workers in Fulton County at State Farm Arena, that Rudy Giuliani equated, made equivalent, with drug dealers, passing around USB ports as if they were heroin, as if it was heroin and cocaine, so linking election workers, Black election workers, with drug dealers. And then those two women receive enormous death threats, death threats that are so horrific that it causes Ruby Freeman to the FBI warns her that she has to leave her home for protection. Thats the kind of violence that this kind of cabal was willing to generate in order to keep Donald Trump in power against the will of the voters. Thats why Georgia is so prominent in this discussion.

AMY GOODMAN: I want to talk about whats just happened, the latest news with Rudy Giuliani, Professor Anderson. In recent weeks, Trumps lawyer, Rudy Giuliani, said he will not contest, so hes admitting that he lied, that he will not contest that he made, quote, false statements about those two Georgia election workers in the aftermath of the 2020 election. I want to go through exactly what youre talking about. Ruby Freeman and Shaye Moss, a mother and daughter, are suing Giuliani for defamation for accusing them of manipulating ballots in Fulton County, Georgia, on Election Day 2020. The Georgia elections board found Giulianis statements to be false and unsubstantiated, according to an investigation by the Georgia elections board. This is California Congressmember Adam Schiff introducing video of Giulianis remarks during that hearing in the House Select Committee to Investigate the January 6th Attack on the Capitol.

REP. ADAM SCHIFF: Id like to show you some of the statements that Rudy Giuliani made in a second hearing before the Georgia state legislators, a week after that video clip from State Farm Arena was first circulated by Mr. Giuliani and President Trump. I want to advise viewers that these statements are completely false and also deeply disturbing.

RUDY GIULIANI: Tape earlier in the day of Ruby Freeman and Shaye Freeman Moss and one other gentleman quite obviously surreptitiously passing around USB ports as if they are vials of heroin or cocaine. I mean, its our its obvious to anyone whos a criminal investigator or prosecutor they are engaged in surreptitious illegal activity, again, that day. And thats a week ago, and theyre still walking around Georgia lying.

AMY GOODMAN: The Black former Georgia state election worker that Giuliani is referring to also testified before the House Select Committee to Investigate the January 6th Attack. This is Shaye Moss being questioned by California Congressmember Adam Schiff.

REP. ADAM SCHIFF: How did you first become aware that Rudy Giuliani, the presidents lawyer, was accusing you and your mother of a crime?

SHAYE MOSS: I was at work, like always, and the former chief, Mr. Jones, asked me to come to his office. And when I went to his office, the former director, Mr. Barron, was in there, and they showed me a video on their computer. It was just like a very short clip of us working at State Farm, and it had someone on the video, like, talking over the video, just saying that we were doing things that we werent supposed to do, just lying throughout the video. And thats when I first found out about it.

REP. ADAM SCHIFF: In one of the videos we just watched, Mr. Giuliani accused you and your mother of passing some sort of USB drive to each other. What was your mom actually handing you on that video?

SHAYE MOSS: A ginger mint.

AMY GOODMAN: So, there you have Shaye Moss. And the way their lives were turned upside down, Professor Anderson, I mean, men coming to their homes demanding they come out, talk about the significance of this. And now its shown that the tape is doctored, and Giuliani is admitting that he lied.

CAROL ANDERSON: Right. And this is and so, this is the kind of terror that is reminiscent of what happened during Reconstruction that led to the KKK Act that Trump is charged with, because that kind of terror was the intimidation of Black people who were exercising their right to vote, the intimidation of Black people who believed that they were American citizens, the intimidation of Black people who were engaged in the electoral process. This is what was happening based on a lie, where Giuliani admits that he lied.

Even worse, I have to say, is that these lies about election fraud, about massive rampant voter fraud, becomes the basis for the voter suppression laws that many states, like Georgia, then put in place. So, youve got an incredible array of laws in place, pieces of those laws dealing with absentee ballots, dealing with drop boxes, dealing with mobile voting units, dealing with places like State Farm, that Fulton County was able to use to deal with the fact that it had to close 90 polling places, and so this was a way to provide a way for people to be able to vote. So, the state using Rudy Giulianis big lie and Donald Trumps big lie to justify shutting down access to the ballot box to minority communities, because the vast number of drop boxes that were shut down after the passage of S.B. 202 were in the Atlanta metropolitan area. So it went from over a hundred drop boxes to fewer than 25 drop boxes.

AMY GOODMAN: And I wanted to ask you about the people involved in these cases, those who are bringing them, judging them. The judge in the new D.C. case is Black. Thats U.S. District Judge Tanya Chutkan, Jamaican American. Now many of those prosecuting Trump are Black. Manhattan DA Alvin Bragg, New York AG Letitia James, Fulton County DA Fani Willis have all received racist threats. And then you have Patrick Labat, the Fulton County sheriff, saying, Hes going to get a mugshot if hes charged in our courts. Can you talk about the significance of this, and then particularly Fani Willis and Labat, who they are, since youre in Atlanta?

CAROL ANDERSON: So, this is why you have this also this kind of massive pushback about Trump cant get a fair trial in D.C., he cant get a fair trial in Manhattan, he cant get a fair trial in Fulton County, because of the Blackness of those spaces and because Black people and Black elected officials are seen as illegitimate. Think about Trump with birtherism, with Obama. That was an attack on Obamas legitimacy, legitimacy as an American citizen, legitimacy as an elected political official.

When Blackness becomes illegitimate so, I think about Mo Brooks, the congressman out of Alabama, who said that if we only count the legal votes, then Trump would be in his second term. So, those legal votes are white peoples votes. The illegal votes are those from African Americans. And so, therefore, folks like Fani Willis, folks like Judge Chutkan, folks like Tish James, folks like Alvin Bragg, theyre not legal, theyre not legitimate, so they can be discounted.

So, when you get a charge that says, I want a change of venue from D.C. to West Virginia, that is sending the signal about the illegitimacy of Black people as American citizens. This, again, is what happened after the Civil War, where the Ku Klux Klan rose up and said, These arent American citizens. The 14th Amendment does not apply to them. The 15th Amendment does not apply to them. We can do to them whatever we want. And thats what youre seeing replicated here in the 21st century.

AMY GOODMAN: So, now, Professor Anderson, theres a lot being made of: All Trump wants to do at this point I mean, hes made history every time here, and now the third indictment, and were expecting to see the fourth any day now in Atlanta is delay these trials, so that if he were to become president, or he had an ally who became president, he could be pardoned. But a president can only pardon on federal crimes.

CAROL ANDERSON: Right.

AMY GOODMAN: Youve got Fani Willis in Atlanta. That is not federal; thats state. So, if you can talk about what were about to see in Atlanta, the grand jury now meeting today?

CAROL ANDERSON: Yeah. So, one of the things that Fani Willis has been really clear on, shes like, Were ready to go. And so, that means, for me, that an indictment is coming soon. And Fani Willis doesnt play. She does not play. And so, you can expect to see a really crisp, clean trial, with locked-in evidence. And if he is convicted here in Georgia, if an indictment comes down and he is convicted, then it means that he wont be able to pardon himself.

And so, part of what I also want to push back on is the assumption that Trump will win the next election. I saw a recent poll that 63% of Americans do not like Donald Trump. And what that means then is that we have the power as American citizens to make sure that this man, who attacked American democracy, who attacked the foundations of the rule of law, does not regain power and have the ability to insert himself in a place where we have an autocracy, where even the memory of a democracy will be abolished. We have the power to stop this thing by registering to vote and by getting out to vote and ensuring that Donald Trump is not the next president of the United States.

AMY GOODMAN: Carol Anderson, I want to thank you for being with us, professor at Emory University, author of many books, including One Person, No Vote: How Voter Suppression Is Destroying Our Democracy.

Coming up, we look at Niger, a week after a military coup ousted the countrys president. One of the coup leaders in Niger has received U.S. military training, had met with a top U.S. officer at the U.S. drone base in Niger just last month. U.S.-trained officers involved with something like 11 coups in Africa over the last decade or so. Stay with us.

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Deep learning algorithm predicts Cardano could surge to $0.50 by September – Finbold – Finance in Bold

Despite Cardano (ADA) taking a cue from Bitcoin (BTC) and the rest of the crypto sector in recent sluggishness, a deep learning algorithm has predicted it still has enough room to recover, perhaps even hitting the price of $0.50 by September 1, 2023.

Indeed, NeuralProphets PyTorch-based prediction algorithm that relies on an open-source machine learning framework has projected that ADA would hit $0.51 in the next month, an increase of 73.4% from its current price, as per the most recent data seen by Finbold on August 4.

Although the above model, which covers the period between January 1 and December 31, 2023, is not an accurate indicator of future prices and should not be taken as such, its predictions have historically proven to be relatively correct.

At the same time, the advanced machine learning algorithms deployed by the cryptocurrency analytics and forecasting platform PricePredictions are more bearish, having set the price of Cardano on September 1, 2023, at $0.275974. according to the latest information.

As things stand, Cardano is currently changing hands at the price of $0.29429, which is an advance of 0.09% in the last 24 hours, a decline of 5.59% across the previous seven days, and a 2.75% gain over the past month, as the charts show.

Meanwhile, the Cardano blockchain development team has continued to make strides, including with the recent launch of Mithril, a stake-based signature protocol to improve the efficiency of the node sync, and its founder Charles Hoskinson debunking the ghost chain myth, all of which could contribute to ADAs price.

Disclaimer: The content on this site should not be considered investment advice. Investing is speculative. When investing, your capital is at risk.

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Deep learning algorithm predicts Cardano could surge to $0.50 by September - Finbold - Finance in Bold

Vision-based dirt distribution mapping using deep learning | Scientific Reports – Nature.com

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Vision-based dirt distribution mapping using deep learning | Scientific Reports - Nature.com

Deep Learning in Medical Applications: Challenges, Solutions, and … – Fagen wasanni

Deep learning (DL), a branch of artificial intelligence (AI), has made significant strides in the medical field. It utilizes artificial neural networks (ANN) to learn from large amounts of data and extract relevant information for various tasks. DL has found applications in imaging diagnosis, clinical and drug research, disease classification and prediction, personalized therapy design, and public health monitoring. The advantages of DL over traditional data analysis methods include improved performance and automation. It also provides evidence-based clinical decision support tools to healthcare professionals.

However, DL presents challenges and limitations. One challenge is the need for quality and representative data. ANNs can fail to generalize when trained on data that does not accurately reflect the problem being addressed. In the medical field, privacy laws like the General Data Protection Regulation (GDPR) restrict the use of clinical data without patient consent. Even with consent, data must be anonymized and ethical approval obtained before use.

Federated learning (FL) offers a solution to these challenges. FL is a privacy-preserving and GDPR-compliant strategy for distributed machine learning. It allows a federation of clients to learn a model without exchanging data. This enables the utilization of vast and diverse medical data available from different sources, increasing the statistical power and generalizability of ML models while addressing privacy, security, and data governance concerns. FL has been successfully applied in various clinical fields, including imaging diagnosis, drug research, and genomics.

Although FL enables data sharing, the lack of explainability in ML models, like ANNs, is a limitation. Explainable AI (XAI) solutions provide tools to interpret and understand ML algorithms. Data type-specific solutions, such as Grad-CAM for image classification, and data type-independent solutions like LIME or NAMs, can be used to enhance interpretability.

Making ML models interpretable is a step towards Trustworthy AI, which ensures reliability and ethicality. XAI helps build robust and ethically sound AI systems.

The CADUCEO project, focused on digestive system diseases, proposes a federated platform that employs FL algorithms. This platform allows medical centers to share knowledge without compromising patient privacy. The project also introduces machine learning algorithms for automated image processing, data augmentation, and diagnosis support.

In conclusion, DL has the potential to improve medical operations in terms of efficiency and treatment quality. With FL and XAI, the challenges associated with data sharing and model interpretability can be addressed, leading to advancements in medical AI applications.

Note: The rest of the article includes details on the materials and methods used, results, functionalities, use cases, and future work.

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Deep Learning in Medical Applications: Challenges, Solutions, and ... - Fagen wasanni

Revolutionizing Telecommunications: The Impact of Deep Learning … – Fagen wasanni

Revolutionizing Telecommunications: The Impact of Deep Learning on Global Connectivity

The telecommunications industry is on the brink of a significant transformation, thanks to the advent of deep learning technologies. Deep learning, a subset of artificial intelligence (AI), is poised to revolutionize global connectivity, bringing about unprecedented changes in the way we communicate and interact with the world.

Deep learning algorithms, which mimic the human brains ability to learn from experience, are being harnessed to improve the efficiency, reliability, and security of telecommunications networks. These algorithms can analyze vast amounts of data, identify patterns, and make predictions, enabling telecom companies to optimize network performance, predict and prevent outages, and enhance customer experience.

One of the most significant impacts of deep learning on telecommunications is in the area of network optimization. Telecom networks generate massive amounts of data every second. Analyzing this data manually to optimize network performance is virtually impossible. However, deep learning algorithms can sift through this data, identify patterns, and make predictions about network performance. This allows telecom companies to proactively address issues, optimize bandwidth allocation, and ensure seamless connectivity for their customers.

Moreover, deep learning is playing a crucial role in enhancing the security of telecommunications networks. Cybersecurity threats are a significant concern for telecom companies, given the sensitive nature of the data they handle. Deep learning algorithms can analyze network traffic, identify unusual patterns, and flag potential security threats. This proactive approach to cybersecurity can help prevent data breaches and protect customer information.

In addition to network optimization and security, deep learning is also transforming customer experience in the telecom sector. Telecom companies are using deep learning algorithms to analyze customer behavior, predict their needs, and personalize their services. This not only enhances customer satisfaction but also helps telecom companies retain their customers and increase their market share.

Furthermore, deep learning is paving the way for the development of advanced telecommunications technologies. For instance, it is playing a crucial role in the development of 5G technology, which promises to revolutionize global connectivity with its high-speed, low-latency connectivity. Deep learning algorithms are being used to optimize the allocation of 5G spectrum, enhance network performance, and ensure seamless connectivity.

However, the integration of deep learning into telecommunications is not without its challenges. Telecom companies need to invest in advanced infrastructure and skilled personnel to harness the power of deep learning. They also need to address concerns related to data privacy and security, given the sensitive nature of the data they handle.

Despite these challenges, the potential benefits of integrating deep learning into telecommunications are immense. It promises to revolutionize global connectivity, enhance customer experience, and pave the way for the development of advanced telecommunications technologies. As such, telecom companies around the world are investing heavily in deep learning, heralding a new era in global connectivity.

In conclusion, deep learning is set to revolutionize the telecommunications industry. Its ability to analyze vast amounts of data, identify patterns, and make predictions can help telecom companies optimize network performance, enhance security, and improve customer experience. While there are challenges to overcome, the potential benefits of integrating deep learning into telecommunications are immense. As we move towards a more connected world, deep learning will play a crucial role in shaping the future of telecommunications.

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The Pros and Cons of Deep Learning | eWeek – eWeek

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Deep learning is an advanced type of artificial intelligence that uses neural networks and complex algorithms to process big data and produce detailed and contextualized outputs, simulating the ways in which human brains process and share information.

This type of artificial intelligence is the foundation for a number of emerging technologies, but despite its many advantages, it also brings forth distinct disadvantages that users need to be aware of.

A quick summary: There are both pros and cons to the practice of deep learning. As far as pros go:users can benefit from a machine learning solution that is highly scalable, automated, hands-off, and capable of producing state-of-the-art AI models, such as large language models. However, the cons are also significant: Deep learning is expensive, consumes massive amounts of power, and creates both ethical and security concerns through its lack of transparency.

Deep learning is a type of artificial intelligence that consists of neural networks with multiple layers, algorithmic training that teaches these neural networks to mimic human brain activity, and training datasets that are massive and nuanced enough to address various AI use cases. Deep learning uses large language models.

Because of its complex neural network architecture, deep learning is a mature form of artificial intelligence that can handle higher-level computation tasks, such as natural language processing, fraud detection, autonomous vehicle driving, and image recognition. Deep learning is one of the core engines running at the heart of generative AI technology.

Examples of deep learning models and their neural networks include the following:

Also see:Generative AI Companies: Top 12 Leaders

Deep learning is a specialized type of machine learning. It has more power and can handle large amounts of different types of data, whereas a typical machine learning model operates on more general tasks and a smaller scale.

Deep learning is primarily used for more complex projects that require human-level reasoning, like designing an automated chatbot or generating synthetic data, for example.

Learn more: Machine Learning vs. Deep Learning

Neural networks constitute a key piece of deep learning model algorithms, creating the human-brain-like neuron pattern that supports deep model training and understanding. A single-layer neural network is whats used in most traditional AI/ML models, but with deep learning models, multiple neural networks are present. A model is not a deep learning model unless it has at least three neural networks, but many deep learning models have dozens of neural networks.

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Deep learning models are designed to handle various inputs and learn through different methods. Many businesses choose to use deep learning models because they can learn and act on tasks independent of hands-on human intervention and data labeling. Their varied learning capabilities also make them great AI models for scalable automation.

Although there are subsets and nuances to each of these learning types, deep learning models can learn through each of the following methods:

Generative AI models are the latest and greatest in the world of artificial intelligence, giving businesses and individuals alike the opportunity to generate original content at scale, usually from natural language inputs.

But these models can only produce logical responses to user queries because of the deep learning and neural network mechanisms that lie at their foundation, allowing them to generate reasonable and contextualized responses on a grand scale and about a variety of topics.

More on this topic: Top 9 Generative AI Applications and Tools

Unstructured datasets especially large unstructured datasets are difficult for most artificial intelligence models to interpret and apply to their training. That means that, in most cases, images, audio, and other types of unstructured data either need to go through extensive labeling and data preparation to be useful, or do not get used at all in training sets.

With deep learning neural networks, unstructured data can be understood and applied to model training without any additional preparation or restructuring. As deep learning models have continued to mature, a number of these solutions have become multimodal and can now accept both structured written content and unstructured image inputs from users.

The neural network design of deep learning models is significant because it gives them the ability to mirror even the most complex forms of human thought and decision-making.

With this design, deep learning models can understand the connections between and the relevance of different data patterns and relationships in their training datasets. This human-like understanding can be used for classification, summarization, quick search and retrieval, contextualized outputs, and more without requiring the model to receive guided training from a human.

Because deep learning models are meant to mimic the human brain and how it operates, these AI models are incredibly adaptable and great multitaskers. This means they can be trained to do more and different types of tasks over time, including complex computations that normal machine learning models cant do and parallel processing tasks.

Through strategies like transfer learning and fine-tuning, a foundational deep learning model can be continually trained and retrained to take on a variety of business and personal use cases and tasks.

Deep learning models require more computing power than traditional machine learning models, which can be incredibly costly and require more hardware and compute resources to operate. These computing power requirements not only limit accessibility but also have severe environmental consequences.

Take generative AI models, for example: Many of these deep learning models have not yet had their carbon footprint tested, but early research about this type of technology suggests that generative AI model emissions are more impactful than many roundtrip airplane fights. While not all deep learning models require the same amount of energy and resources that generative AI models do, they still need more than the average AI tool to perform their complex tasks.

Deep learning models are typically powered with graphics processing units (GPUs), specialized chips, and other infrastructure components that can be quite expensive, especially at the scale that more advanced deep learning models require.

Because of the quantity of hardware these models need to operate, theres been a GPU shortage for several years, though some experts believe this shortage is coming to an end. Additionally, only a handful of companies make this kind of infrastructure. Without the right quantity and types of infrastructure components, deep learning models cannot run.

Data scientists and AI specialists more than likely know whats in the training data for deep learning models. However, especially for models that learn through unsupervised learning, these experts may not fully understand the outputs that come out of these models or the processes deep learning models follow to get those results.

As a consequence, users of deep learning models have even less transparency and understanding of how these models work and deliver their responses, making it difficult for anyone to do true quality assurance.

Even though deep learning models can work with data in varying formats, both unstructured and structured, these models are only as good as the data and training they receive.

Training and datasets need to be unbiased, datasets need to be large and varied, and raw data cant contain errors. Any erroneous training data, regardless of how small the error, could be magnified and made worse as models are fine-tuned and scaled.

Deep learning models have introduced a number of security and ethical concerns into the AI world. They offer limited visibility into their training practices and data sources, which opens up the possibility of personal data and proprietary business data getting into training sets without permission.

Unauthorized users could get access to highly sensitive data, leading to cybersecurity issues and other ethical use concerns.

More on a similar topic: Generative AI Ethics: Concerns and Solutions

Deep learning is a powerful artificial intelligence tool that requires dedicated resources and raises some significant concerns. However, the pros outweigh the cons at this point, as deep learning gives businesses the technology backbone they need to develop and run breakthrough solutions for everything from new pharmaceuticals to smart city infrastructure.

The best path forward is not to get rid of or limit deep learnings capabilities but rather to develop policies and best practices for using this technology in a responsible way.

Read next: 100+ Top Artificial Intelligence (AI) Companies

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The Pros and Cons of Deep Learning | eWeek - eWeek

The Promise of AI EfficientNet: Advancements in Deep Learning and … – Fagen wasanni

Exploring the Potential of AI EfficientNet: Breakthroughs in Deep Learning and Computer Vision

Artificial intelligence (AI) has come a long way in recent years, with advancements in deep learning and computer vision leading the charge. One of the most promising developments in this field is the AI EfficientNet, a family of advanced deep learning models that have the potential to revolutionize various industries and applications. In this article, we will explore the potential of AI EfficientNet and discuss some of the breakthroughs it has made in deep learning and computer vision.

Deep learning, a subset of machine learning, involves training artificial neural networks to recognize patterns and make decisions based on large amounts of data. One of the most significant challenges in deep learning is creating models that are both accurate and efficient. This is where AI EfficientNet comes in. Developed by researchers at Google AI, EfficientNet is a family of models that are designed to be both highly accurate and computationally efficient. This is achieved through a technique called compound scaling, which involves scaling the depth, width, and resolution of the neural network simultaneously.

The development of AI EfficientNet has led to several breakthroughs in deep learning and computer vision. One of the most notable achievements is the improvement in image classification accuracy. EfficientNet models have been able to achieve state-of-the-art accuracy on the ImageNet dataset, a widely used benchmark for image classification algorithms. This is particularly impressive considering that EfficientNet models are significantly smaller and faster than other leading models, making them more suitable for deployment on devices with limited computational resources, such as smartphones and IoT devices.

Another significant breakthrough made possible by AI EfficientNet is the improvement in object detection and segmentation. These tasks involve identifying and locating objects within an image and are crucial for applications such as autonomous vehicles, robotics, and surveillance systems. EfficientNet models have been combined with other deep learning techniques, such as the Focal Loss and the Feature Pyramid Network, to create state-of-the-art object detection and segmentation systems. These systems have achieved top performance on benchmark datasets such as COCO and PASCAL VOC, demonstrating the potential of AI EfficientNet in these critical applications.

The advancements made by AI EfficientNet in deep learning and computer vision have far-reaching implications for various industries and applications. In healthcare, for example, EfficientNet models can be used to improve the accuracy of medical image analysis, enabling faster and more accurate diagnosis of diseases. In agriculture, these models can be used to analyze satellite imagery and identify areas that require attention, such as regions affected by pests or diseases. In retail, AI EfficientNet can be used to improve the accuracy of visual search engines, making it easier for customers to find the products they are looking for.

Furthermore, the efficiency of AI EfficientNet models makes them ideal for deployment on edge devices, such as smartphones, drones, and IoT devices. This opens up new possibilities for real-time applications, such as facial recognition, object tracking, and augmented reality. By bringing advanced deep learning capabilities to these devices, AI EfficientNet has the potential to transform the way we interact with technology and the world around us.

In conclusion, AI EfficientNet represents a significant breakthrough in deep learning and computer vision, offering state-of-the-art accuracy and efficiency in a range of applications. From healthcare to agriculture, retail to edge devices, the potential of AI EfficientNet is vast and exciting. As researchers continue to refine and expand upon this technology, we can expect to see even more impressive advancements in the field of artificial intelligence, ultimately leading to a more connected, intelligent, and efficient world.

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The Intersection of AI Deep Learning and Quantum Computing: A … – Fagen wasanni

Exploring the Synergy between AI Deep Learning and Quantum Computing: Unleashing New Possibilities

The intersection of artificial intelligence (AI) deep learning and quantum computing is creating a powerful partnership that promises to revolutionize the way we solve complex problems and transform industries. As we continue to explore the synergy between these two cutting-edge technologies, we are witnessing the emergence of new possibilities and applications that were once considered science fiction.

AI deep learning, a subset of machine learning, involves the use of artificial neural networks to enable machines to learn and make decisions without explicit programming. This technology has already made significant strides in areas such as image and speech recognition, natural language processing, and autonomous vehicles. However, the computational power required to process and analyze the vast amounts of data involved in deep learning is immense, and this is where quantum computing comes into play.

Quantum computing, which leverages the principles of quantum mechanics, has the potential to solve problems that are currently intractable for classical computers. Unlike classical computers that use bits to represent information as 0s and 1s, quantum computers use quantum bits, or qubits, which can represent both 0 and 1 simultaneously. This allows quantum computers to perform multiple calculations at once, exponentially increasing their processing power.

The convergence of AI deep learning and quantum computing is expected to unlock new possibilities in various fields. For instance, in drug discovery, quantum computing can be used to simulate and analyze complex molecular structures, while AI deep learning can help identify patterns and predict the effectiveness of potential treatments. This powerful combination could significantly accelerate the drug discovery process, ultimately leading to more effective treatments for a wide range of diseases.

In the field of finance, quantum computing can optimize trading strategies and risk management, while AI deep learning can analyze large datasets to predict market trends and identify investment opportunities. Together, these technologies could revolutionize the financial industry by providing more accurate predictions and enabling faster, more informed decision-making.

Moreover, the partnership between AI deep learning and quantum computing has the potential to enhance cybersecurity. Quantum computers can efficiently solve complex cryptographic problems, while AI deep learning can detect and respond to cyber threats in real-time. This combination could lead to the development of more secure communication systems and robust defense mechanisms against cyberattacks.

However, the integration of AI deep learning and quantum computing is not without its challenges. One of the main hurdles is the current lack of mature quantum hardware, as quantum computers are still in their infancy and not yet capable of outperforming classical computers in most tasks. Additionally, developing algorithms that can harness the full potential of quantum computing for AI deep learning is a complex task that requires a deep understanding of both fields.

Despite these challenges, researchers and tech giants such as Google, IBM, and Microsoft are investing heavily in the development of quantum computing and AI deep learning technologies. As these efforts continue, we can expect to see significant advancements in the coming years that will further strengthen the partnership between AI deep learning and quantum computing.

In conclusion, the intersection of AI deep learning and quantum computing holds immense promise for solving complex problems and transforming industries. By harnessing the power of these two cutting-edge technologies, we can unlock new possibilities and applications that will shape the future of technology and innovation. As we continue to explore the synergy between AI deep learning and quantum computing, we are poised to witness a technological revolution that will redefine the boundaries of what is possible.

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The Intersection of AI Deep Learning and Quantum Computing: A ... - Fagen wasanni