Quotes On Business Intelligence – The progress of AI is inevitable, and it is unclear how it will turn out for humanity. Some believe we can look forward to a great future, while others believe it means we’re on our way to being replaced by robots. A third perspective is aware of the risks but sees them as manageable.
We hear a lot about artificial intelligence (AI) and its transformative potential. But what this means for the future of humanity is not entirely clear. Not everyone always has the same definition in mind when they use the term artificial intelligence. Even after determining what possibilities are achievable, questions arise about the consequences of those possibilities.
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Some futurists believe that life will improve with the beast, while others believe that it is in serious danger. There is a wide range between the two extremes. Here are some takeaways from 11 experts across the spectrum.
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The popular imagination’s association with artificial intelligence, a synthetic entity with its own consciousness, still exists in the realm of science fiction.
Richard Socher is now the CEO of you.com, the new trusted search engine. He wrote this quote a few years ago when he was an EVP at Salesforce.
John Fremont is the co-founder and chief strategy officer of Hypergiant. He spoke at a Salesforce-sponsored presentation titled “Building a Better World: AI and the Future of Business.”
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While “tapestry” has a beautiful meaning for compositional work, there are those like Tristan Harris who see it as a monstrous synthesis rather than one:
This idea is well mentioned in the article “How to prevent technology from becoming a digital Frankenstein”. Harris is the founder and executive director of the Center for Humane Technologies. He elaborates on his concerns in a series of conversations with Tristan Harris called The Humanities: A New Agenda for Technology, available on Vimeo.
Others worry about something powerful growing and expanding unchecked. But some say we worry about the wrong things:
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Andrew Ng, founder and CEO of Landing AI and founder of deeplearning.ai, also teaches at Stanford. This quote is from Andrew Ng’s article, “Why Artificial Intelligence Is the New Electricity,” reflecting a talk he gave to Stanford Business School students as part of a series presented by the Stanford MSx Program.
He says the focus should be on the impact on society, adding that the “hype about bad AI” draws attention to a more pressing problem: job displacement.
James Manika is the chairman and director of the McKinsey Global Institute (MGI). At Salesforce’s “Future of Work in the Age of Artificial Intelligence” panel, he expressed his concern about how people can adapt to the new future by acquiring the right skills.
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In fact, advances in automation combined with robotics bring Ng’s warning into modern reality:
Below is the full excerpt from National Geographic’s The Robot Revolution Has Arrived.
“We’re used to machine intelligence that we can take with us,” said Manuela Veloso, an artificial intelligence roboticist at Carnegie Mellon University in Pittsburgh. He held up his smartphone. “Now we will have to get used to the fact that the body and mind move without us.”
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On the other hand, some people believe that more cars are a good thing, and that the job displacement more than offsets the job creation.
Svitlana Sikular is Gartner’s vice president of research, and her statement was published in a press release: Gartner says that by 2020, artificial intelligence will create more jobs than it will destroy. While acknowledging that AI will lead to the loss of millions of jobs, he also sees benefits in improving productivity and transitioning to new jobs.
Some require advanced skills, but jobs that don’t require such advanced skills will remain open, he said. The main losses will be in middle and lower-level positions, he predicts.
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Another issue with artificial intelligence that has been in the spotlight for the past few years is its “black box” nature, as business decisions are determined by algorithms. (Also read some comments on artificial intelligence)
It’s not just people in the industry who worry about making decisions with programs that don’t have to consider decisions and consequences. Lawyers fighting for the rights of their clients must contend with organizations that argue that decisions to terminate services are made not by humans, but by algorithms.
Michelle Gilman, professor of clinical law at the University of Baltimore, recently appeared in MIT’s Technology Review on the coming war against hidden algorithms that drive people into poverty.
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“A growing body of lawyers is exposing, managing and challenging the automated systems that deprive the poor of housing, jobs and basic services.”
Gilman was surprised that as her disabled elderly client’s illness worsened, she was assigned fewer hours of care, not more. When they arrived at the hearing, the state representative revealed that the nurse was using a new algorithm. Gilman recalled:
“He couldn’t answer what factors go into it. How weighted is it? What outcome are you looking for? So I have my law student with me at the hospital. It’s like, ‘Oh,’ and I’m going to cross-check the algorithm.” Hi there?”
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Because algorithms have the power to decide who gets life-saving services and who doesn’t, it’s important to understand the processes they use. Despite their scientific and realistic appearance, not all algorithmic models are truly reliable.
Oleksandr Amini is a computer scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). This quote is from a late November 2020 MIT News article, “Neural networks learn when not to trust”
He goes into detail about the inseparability of 100% dependence on something 100% unreliable. He says, “Neural networks are very good at knowing the right answer 99 percent of the time.” But even one percent error is unacceptable when human life is at stake.
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That’s a problem because in the past we’ve typically developed our models without identifying when they might be wrong,” Amini explained. “We’re really concerned about that 1% and how we can detect them. , effectively provide”.
Vivien Min is the CEO and co-founder of Socos Labs. Inc. Magazine also named her one of 10 women to watch in tech. He shared his views on artificial intelligence in his talk “Understand Your Love/Hate Relationship with Artificial Intelligence”. (Also read: Fairness in Machine Learning: Eliminating Data Bias.)
Kathy Baxter’s specialty is just that. He is a practicing Ethical AI architect at Salesforce. In an interview on Salesforce’s blog, he shared his understanding of “the biases that live in our data.” His solution requires diversity for the same reasons discussed in Why Diversity Matters for Quality Data in AI Learning.
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Large companies now have experts, even entire departments, dedicated to maintaining the ethics of AI. But is it sincere, or does it live up to the standards they hope to uphold?
Timnit Gebru, “On the Perils of Stochastic Repetition: Can Language Models Be Too Big?” This article has not been published.
The ideas outlined in the paper could credit Gebre for his work on Google’s AI ethics group. On December 2, he tweeted that the tech giant was accepting his “resignation.”
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Gebru, co-authored by Joy Buolamwini, Shades of Gender: Intersectional Accuracy Gaps in Commercial Gender Categorization (see also: Can AI be biased?) facial recognition was fairly accurate for white men, but not for women or people of color showed, which was low. Google was proud to have him on the team, but he dismissed the results of the latest research.
In reporting on his forced resignation from Google, the MIT Technology Review also discussed the content of the controversial document: “Hebru’s draft document suggests that the vast resources required to build and maintain such large-scale AI models tend to benefit wealthy organizations . marginalized communities have the hardest time.”
Focusing on these issues, the article raises some ideas that the company may prefer to sweep under the rug, including issues of “negative environmental impact and unequal access to resources” that reinforce the gap between the rich and the poor. .
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Emily Bender, a professor of computational linguistics at the University of Washington, is one of seven co-authors on the paper. He contributed a draft article to the MIT Technology Review. Bender explains that you need to think about the consequences, not just move forward.
Ariella Brown has been writing about technology and marketing since 2010, covering everything from analytics to virtual reality. Prior to that, he earned a doctorate in English and taught, initiated, and published writing courses at the college level.
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