Artificial Intelligence / Machine Learning / Deep Learning

Getting smart about the future of AI [MIT Technology Review Insights]

On 02/21/2019, in 21st century, Artificial Intelligence / Machine Learning / Deep Learning, business, cognitive computing, corporate / business world, emerging technologies, future, game-changing environment, history, society, tools, United States, universities, vendors, workplace, by Daniel Christian

Getting smart about the future of AI — from technologyreview.com by MIT Technology Review Insights
Artificial intelligence is a primary driver of possibilities and promise as the Fourth Industrial Revolution unfolds.

Excerpts:

The Industrial Revolution conjures up images of steam engines, textile mills, and iron workers. This was a defining period during the late 18th and early 19th centuries, as society shifted from primarily agrarian to factory-based work. A second phase of rapid industrialization occurred just before World War I, driven by growth in steel and oil production, and the emergence of electricity.

Fast-forward to the 1980s, when digital electronics started having a deep impact on society—the dawning Digital Revolution. Building on that era is what’s called the Fourth Industrial Revolution. Like its predecessors, it is centered on technological advancements—this time it’s artificial intelligence (AI), autonomous machines, and the internet of things—but now the focus is on how technology will affect society and humanity’s ability to communicate and remain connected.

That’s what AI technologies represent in the current period of technological change. It is now critical to carefully consider the future of AI, what it will look like, the effect it will have on human life, and what challenges and opportunities will arise as it evolves.

See the full report here >>

Also see:

Where Next for AI In Business? An overview for C-level executives — from techrevolution.asia by Bernard Marr
Excerpt:
The AI revolution is now well underway. In finance, marketing, medicine and manufacturing, machines are learning to monitor and adapt to real-world inputs in order to operate more efficiently, without human intervention. In our everyday lives, AI kicks in whenever we search the internet, shop online or settle down on the sofa to watch Netflix or listen to Spotify. At this point, it’s safe to say that AI is no longer the preserve of science fiction, but has already changed our world in a huge number of different ways.So: what next? Well, the revolution is showing no signs of slowing down. Research indicates that businesses, encouraged by the initial results they have seen, are now planning on stepping up investment and deployment of AI.One of the most noticeable advances will be the ongoing “democratization” of AI. What this means, put simply, is that AI-enabled business tools will increasingly become available to all of us, no matter what jobs we do.

You’ll no longer need to be an expert in computer science to use AI to do your job efficiently – this is the “democratization” of AI and it’s a trend which will impact more and more businesses going forward.

Reflections on two articles involving the downsides of/warnings re: AI

On 02/16/2019, in 21st century, algorithms, Artificial Intelligence / Machine Learning / Deep Learning, Asia, bots, Daniel S. Christian, data mining, ethics, Google, Law Schools, legislatures / government / legal, Microsoft, morals/values, society, technology (general), the downsides of technology, tools, by Daniel Christian

Google and Microsoft warn that AI may do dumb things — from wired.com by Tom Simonite

Excerpt:

Alphabet likes to position itself as a leader in AI research, but it was six months behind rival Microsoft in warning investors about the technology’s ethical risks. The AI disclosure in Google’s latest filing reads like a trimmed down version of much fuller language Microsoft put in its most recent annual SEC report, filed last August:

“AI algorithms may be flawed. Datasets may be insufficient or contain biased information. Inappropriate or controversial data practices by Microsoft or others could impair the acceptance of AI solutions. These deficiencies could undermine the decisions, predictions, or analysis AI applications produce, subjecting us to competitive harm, legal liability, and brand or reputational harm.”

Chinese company leaves Muslim-tracking facial recognition database exposed online — from by Catalin Cimpanu
Researcher finds one of the databases used to track Uyghur Muslim population in Xinjiang.

Excerpt:

One of the facial recognition databases that the Chinese government is using to track the Uyghur Muslim population in the Xinjiang region has been left open on the internet for months, a Dutch security researcher told ZDNet.

The database belongs to a Chinese company named SenseNets, which according to its website provides video-based crowd analysis and facial recognition technology.
…
The user data wasn’t just benign usernames, but highly detailed and highly sensitive information that someone would usually find on an ID card, Gevers said. The researcher saw user profiles with information such as names, ID card numbers, ID card issue date, ID card expiration date, sex, nationality, home addresses, dates of birth, photos, and employer.
…
Some of the descriptive names associated with the “trackers” contained terms such as “mosque,” “hotel,” “police station,” “internet cafe,” “restaurant,” and other places where public cameras would normally be found.

From DSC:
Readers of this blog will know that I’m generally pro-technology. But especially focusing in on that last article, to me, privacy is key here. For which group of people from which nation is next? Will Country A next be tracking Christians? Will Country B be tracking people of a given sexual orientation? Will Country C be tracking people with some other characteristic?

Where does it end? Who gets to decide? What will be the costs of being tracked or being a person with whatever certain characteristic one’s government is tracking? What forums are there for combating technologies or features of technologies that we don’t like or want?

We need forums/channels for raising awareness and voting on these emerging technologies. We need informed legislators, senators, lawyers, citizens…we need new laws here…asap.

Wearable, instant language translation on the go

On 02/15/2019, in Artificial Intelligence / Machine Learning / Deep Learning, global / globalization, human-computer interaction (HCI), intelligent systems, Languages and translation, Natural Language Processing (NLP), society, tools, vendors, voice recognition / voice enabled interfaces, wearables, by Daniel Christian

Instant translation on the go #AI #MachineLearning #voice
RT @helene_wpli @BrianRoemmele @jblefevre60 @akwyz @DimitriHommel @alvinfoo @floriansemle @dinisguarda @YuHelenYu @kashthefuturist @MarshaCollier @CurtisSChin @Xbond49 @guzmand @kalydeoo @Ym78200 @ArnaultChatel pic.twitter.com/XcUiX9oNdz

— Olivier Laborde ????? (@labordeolivier) February 13, 2019

The real reason tech struggles with algorithmic bias — from wired.com by Yael Eisenstat

Excerpts:

ARE MACHINES RACIST? Are algorithms and artificial intelligence inherently prejudiced? Do Facebook, Google, and Twitter have political biases? Those answers are complicated.

But if the question is whether the tech industry doing enough to address these biases, the straightforward response is no.
…
Humans cannot wholly avoid bias, as countless studies and publications have shown. Insisting otherwise is an intellectually dishonest and lazy response to a very real problem.
…
In my six months at Facebook, where I was hired to be the head of global elections integrity ops in the company’s business integrity division, I participated in numerous discussions about the topic. I did not know anyone who intentionally wanted to incorporate bias into their work. But I also did not find anyone who actually knew what it meant to counter bias in any true and methodical way.

But the company has created its own sort of insular bubble in which its employees’ perception of the world is the product of a number of biases that are engrained within the Silicon Valley tech and innovation scene.

Making New Drugs With a Dose of Artificial Intelligence [Metz]

On 02/11/2019, in algorithms, Artificial Intelligence / Machine Learning / Deep Learning, chemistry, corporate / business world, emerging technologies, Google, health, by Daniel Christian

Making New Drugs With a Dose of Artificial Intelligence — from nytimes.com by Cade Metz

Excerpt:

DeepMind specializes in “deep learning,” a type of artificial intelligence that is rapidly changing drug discovery science. A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines. These A.I. techniques can speed up many aspects of drug discovery and, in some cases, perform tasks typically handled by scientists.

“It is not that machines are going to replace chemists,” said Derek Lowe, a longtime drug discovery researcher and the author of In the Pipeline, a widely read blog dedicated to drug discovery. “It’s that the chemists who use machines will replace those that don’t.”

Reflections on “135 Million Reasons To Believe In A Blockchain Miracle” [Maddock]

On 02/09/2019, in 21st century, Artificial Intelligence / Machine Learning / Deep Learning, Blockchain, change, changing business models, emerging technologies, India, internet, Law Schools, leadership, legislatures / government / legal, new business models, political science, by Daniel Christian

135 Million Reasons To Believe In A Blockchain Miracle — from forbes.com by Mike Maddock

Excerpts:

Which brings us to the latest headlines about a cryptocurrency entrepreneur’s passing—taking with him the passcode to unlock C$180 million (about $135 million U.S.) in investor currency—which is now reportedly gone forever. Why? Because apparently, the promise of blockchain is true: It cannot be hacked. It is absolutely trustworthy.

Gerald Cotton, the CEO of a crypto company, reportedly passed away recently while building an orphanage in India. Unfortunately, he was the only person who knew the passcode to access the millions his investors had entrusted in him.

…

This is how we get the transition to Web 3.0.

Some questions to consider:

Who will build an easy-to-use “wallet” of the future?
Are we responsible enough to handle that much power?

Perhaps the most important question of all is: What role do our “trusted” experts play in this future?

From DSC:
I’d like to add another question to Mike’s article:

How should law schools, law firms, legislative bodies, government, etc. deal with the new, exponential pace of change and with the power of emerging technologies like #blockchain , #AI, #ML , #FacialRecognition etc.?

Google is bringing translation to its Home speakers [Newman]

On 02/08/2019, in 21st century, A/V -- audio/visual, algorithms, Artificial Intelligence / Machine Learning / Deep Learning, bots, Google, innovation, Languages and translation, Natural Language Processing (NLP), platforms, Technologies for your home, voice recognition / voice enabled interfaces, by Daniel Christian

Google is bringing translation to its Home speakers — from businessinsider.com by Peter Newman

Excerpt:

Google has added real-time translation capabilities to its Google Home smart speakers, the Home Hub screened speaker, as well as other screened devices from third parties, according to Android Police.

Also see:

Google Assistant is now your multilingual interpreter — from androidpolice.com by Hagop Kavafian

AI bias: 9 questions leaders should ask [Casey]

On 02/01/2019, in 21st century, algorithms, analytics, Artificial Intelligence / Machine Learning / Deep Learning, bots, cognitive computing, corporate / business world, emerging technologies, human-computer interaction (HCI), ideas, intelligent systems, MBA, organizational change, platforms, workplace, by Daniel Christian

AI bias: 9 questions leaders should ask — from enterprisersproject.com by Kevin Casey
Artificial intelligence bias can create problems ranging from bad business decisions to injustice. Use these questions to fight off potential biases in your AI systems.

Excerpt:

People questions to ask about AI bias
1. Who is building the algorithms?
2. Do your AI & ML teams take responsibility for how their work will be used?
3. Who should lead an organization’s effort to identify bias in its AI systems?
4. How is my training data constructed?

Data questions to ask about AI bias
5. Is the data set comprehensive?
6. Do you have multiple sources of data?

Management questions to ask about AI bias
7. What proportion of resources is appropriate for an organization to devote to assessing potential bias?
8. Have you thought deeply about what metrics you use to evaluate your work?
9. How can we test for bias in training data?

13 industries soon to be revolutionized by artificial intelligence [Forbes Technology Council]

On 01/30/2019, in 21st century, algorithms, Artificial Intelligence / Machine Learning / Deep Learning, business, cognitive computing, corporate / business world, education, education technology, emerging technologies, health, internet, IT in HE, Nursing, trends, United States, workplace, by Daniel Christian

13 industries soon to be revolutionized by artificial intelligence — from forbes.com by the Forbes Technology Council

Excerpt:

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today’s world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works.

While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI.

Emerging technology trends can seem both elusive and ephemeral, but some become integral to business and IT strategies—and form the backbone of tomorrow’s technology innovation. The eight chapters of Tech Trends 2019 look to guide CIOs through today’s most promising trends, with an eye toward innovation and growth and a spotlight on emerging trends that may well offer new avenues for pursuing strategic ambitions.

Learning Ecosystems

Getting smart about the future of AI [MIT Technology Review Insights]

Reflections on two articles involving the downsides of/warnings re: AI

Wearable, instant language translation on the go

The real reason tech struggles with algorithmic bias [Eisenstat]

Making New Drugs With a Dose of Artificial Intelligence [Metz]

Reflections on “135 Million Reasons To Believe In A Blockchain Miracle” [Maddock]

Google is bringing translation to its Home speakers [Newman]

AI bias: 9 questions leaders should ask [Casey]

13 industries soon to be revolutionized by artificial intelligence [Forbes Technology Council]

Tech Trends 2019 [Deloitte Insights]

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