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The importance of data to today’s businesses can’t be overstated. Studies show data-driven companies are 58% more likely to beat revenue goals than non-data-driven companies and 162% more likely to significantly outperform laggards. Data analytics are helping nearly half of all companies make better decisions about everything, from the products they deliver to the markets they target. Data is becoming critical in every industry, whether it’s helping farms increase the value of the crops they produce or fundamentally changing the game of basketball.

Used optimally, data is nothing less than a critically important asset. Problem is, it’s not always easy to put data to work. The Seagate Rethink Data report, with research and analysis by IDC, found that only 32% of the data available to enterprises is ever used and the remaining 68% goes unleveraged. Executives aren’t fully confident in their current ability—nor in their long-range plans—to wring optimal levels of value out of the data they produce, acquire, manage, and use.

What’s the disconnect? If data is so important to a business’s health, why is it so hard to master?

In the best-run companies, the systems that connect data producers and data consumers are secure and easy to deploy. But they’re usually not. Companies are challenged with finding data and leveraging it for strategic purposes. Sources of data are hard to identify and even harder to evaluate. Datasets used to train AI models for the automation of tasks can be hard to validate. Hackers are always looking to steal or compromise data. And finding quality data is a challenge for even the savviest data scientists. 

The lack of an end-to-end system for ensuring high-quality data and sharing it efficiently has indirectly delayed the adoption of AI.

Communication gaps can also derail the process of delivering impactful insights. Executives who fund data projects and the data engineers and scientists who carry them out don’t always understand one another. These data practitioners can create a detailed plan, but if the practitioner doesn’t frame the results properly, the business executive who requested them may say they were looking for something different. The project will be labeled a failure, and the chance to generate value out of the effort will fall by the wayside.

Companies encounter data issues, no matter where they are in terms of data maturity. They’re trying to figure out ways to make data an important part of their future, but they’re struggling to put plans into practice.

If you’re in this position, what do you do?

Companies found themselves at a similar inflection point back in the 2010s, trying to sort out their places in the cloud. They took years developing their cloud strategies, planning their cloud migrations, choosing platforms, creating Cloud Business Offices, and structuring their organizations to best take advantage of cloud-based opportunities. Today, they’re reaping the benefits: Their moves to the cloud have enabled them to modernize their apps and IT systems.

Enterprises now have to make similar decisions about data. They need to consider many factors to make sure data is providing a foundation for their business going forward. They should ask questions such as:

Is the data the business needs readily available?What types of sources of data are needed? Are there distributed and diverse sets of data you don’t know about?Is the data clean, current, reliable, and able to integrate with existing systems?Is the rest of the C-level onboard with the chief data officer’s approach?Are data scientists and end users communicating effectively about what’s needed and what’s being delivered?How is data being shared?How can I trust my data?Does every person and organization that needs access to the data have the right to use it?

This is about more than just business intelligence. It’s about taking advantage of an opportunity that’s taking shape. Data use is exploding, tools to leverage it are becoming more efficient, and data scientists’ expertise is growing. But data is hard to master. Many companies aren’t set up to make the best use of the data

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By: Janice Zdankus, Anthony Delli Colli
Title: Getting the most from your data-driven transformation: 10 key principles
Sourced From: www.technologyreview.com/2021/10/14/1037054/getting-the-most-from-your-data-driven-transformation-10-key-principles/
Published Date: Thu, 14 Oct 2021 16:08:28 +0000

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Finding value in generative AI for financial services

UBS report cover

With tools such as ChatGPT, DALLE-2, and CodeStarter, generative AI has captured the public imagination in 2023. Unlike past technologies that have come and gone—think metaverse—this latest one looks set to stay. OpenAI’s chatbot, ChatGPT, is perhaps the best-known generative AI tool. It reached 100 million monthly active users in just two months after launch, surpassing even TikTok and Instagram in adoption speed, becoming the fastest-growing consumer application in history.

UBS report cover 1

According to a McKinsey report, generative AI could add $2.6 trillion to $4.4 trillion annually in value to the global economy. The banking industry was highlighted as among sectors that could see the biggest impact (as a percentage of their revenues) from generative AI. The technology “could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented,” says the report.

For businesses from every sector, the current challenge is to separate the hype that accompanies any new technology from the real and lasting value it may bring. This is a pressing issue for firms in financial services. The industry’s already extensive—and growing—use of digital tools makes it particularly likely to be affected by technology advances. This MIT Technology Review Insights report examines the early impact of generative AI within the financial sector, where it is starting to be applied, and the barriers that need to be overcome in the long run for its successful deployment.

DOWNLOAD THE REPORT

The main findings of this report are as follows:

Corporate deployment of generative AI in financial services is still largely nascent. The most active use cases revolve around cutting costs by freeing employees from low-value, repetitive work. Companies have begun deploying generative AI tools to automate time-consuming, tedious jobs, which previously required humans to assess unstructured information.
UBS web ready 5
There is extensive experimentation on potentially more disruptive tools, but signs of commercial deployment remain rare. Academics and banks are examining how generative AI could help in impactful areas including asset selection, improved simulations, and better understanding of asset correlation and tail risk—the probability that the asset performs far below or far above its average past performance. So far, however, a range of practical and regulatory challenges are impeding their commercial use.Legacy technology and talent shortages may slow adoption of generative AI tools, but only temporarily. Many financial services companies, especially large banks and insurers, still have substantial, aging information technology and data structures, potentially unfit for the use of modern applications. In recent years, however, the problem has eased with widespread digitalization and may continue to do so. As is the case with any new technology, talent with expertise specifically in generative AI is in short supply across the economy. For now, financial services companies appear to be training staff rather than bidding to recruit from a sparse specialist pool. That said, the difficulty in finding AI talent is already starting to ebb, a process that would mirror those seen with the rise of cloud and other new technologies.
UBS web ready 2
More difficult to overcome may be weaknesses in the technology

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By: MIT Technology Review Insights
Title: Finding value in generative AI for financial services
Sourced From: www.technologyreview.com/2023/11/26/1083841/finding-value-in-generative-ai-for-financial-services/
Published Date: Mon, 27 Nov 2023 01:00:00 +0000

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The Download: unpacking OpenAI Q* hype, and X’s financial woes

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This is today’s edition of The Download our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Unpacking the hype around OpenAI’s rumored new Q* model

Ever since last week’s dramatic events at OpenAI, the rumor mill has been in overdrive about why the company’s board tried to oust CEO Sam Altman.

While we still don’t know all the details, there have been reports that researchers at OpenAI had made a “breakthrough” in AI that alarmed staff members. The claim is that they came up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school level math.

Some at OpenAI reportedly believe this could be a breakthrough in the company’s quest to build artificial general intelligence, a much-hyped concept of an AI system that is smarter than humans.

So what’s actually going on? And why is grade-school math such a big deal? Our senior AI reporter Melissa Heikkilä called some experts to find out how big of a deal any such breakthrough would really be. Here’s what they had to say.

This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 X is hemorrhaging millions in advertising revenue 
Internal documents show the company is in an even worse position than previously thought. (NYT $)
 Misinformation ‘super-spreaders’ on X are reportedly eligible for payouts from its ad revenue sharing program. (The Verge)
It’s not just you: tech billionaires really are becoming more unbearable. (The Guardian)

2 The brakes seem to now be off on AI development 
📈
With Sam Altman’s return to OpenAI, the ‘accelerationists’ have come out on top. (WSJ $)
Inside the mind of OpenAI’s chief scientist, Ilya Sutskever. (MIT Technology Review)

3 How Norway got heat pumps into two-thirds of its households
Mostly by making it the cheaper choice for people. (The Guardian)
Everything you need to know about the wild world of heat pumps. (MIT Technology Review)

4 How your social media feeds shape how you see the Israel-Gaza war
Masses of content are being pumped out, rarely with any nuance or historical understanding. (BBC)
China tried to keep kids off social media. Now the elderly are hooked. (Wired $)

5 US regulators have surprisingly little scope to enforce Amazon’s safety rules
As demonstrated by the measly $7,000 fine issued by Indiana after a worker was killed by warehouse machinery. (WP $)

6 How Ukraine is using advanced technologies on the battlefield 
The Pentagon is using the conflict as a testbed for some of the 800-odd AI-based projects it has in progress. (AP $)
Why business is booming for military AI startups. (MIT Technology Review)

7 Shein is trying to overhaul its image, with limited success
Its products seem too cheap to be ethically sourced—and it doesn’t take kindly to people pointing that out. (The Verge)
 Why my bittersweet relationship with Shein had to end. (MIT Technology Review)

8 Every app can be a dating app now 
💑
As people turn their backs on the traditional apps, they’re finding love in places like Yelp, Duolingo and Strava. (WSJ $)
+ Job sharing apps are also becoming more popular. (BBC)

9 People can’t get enough of work livestreams on TikTok
It’s mostly about the weirdly hypnotic quality of watching people doing tasks like manicures or frying eggs. (The Atlantic $)

10 A handy guide to time travel in the movies
Whether you prioritize scientific accuracy or entertainment value, this chart has got you covered. (Ars Technica)

Quote of the day

“It’s in the AI industry’s interest to make people think that only the big players can do this—but it’s not true.”

—Ed Newton-Rex, who just resigned as VP of audio at Stability.AI, says the idea that generative AI models can only be built by scraping artists’ work is a myth in an interview with The Next Web. 

The big story

The YouTube baker fighting back against deadly “craft hacks”

rainbow glue coming out of a hotglue gun onto a toothbrush, surrounded by caution tape
STEPHANIE ARNETT/MITTR | ENVATO, GETTY

September 2022

Ann Reardon is probably the last person you’d

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By: Charlotte Jee
Title: The Download: unpacking OpenAI Q* hype, and X’s financial woes
Sourced From: www.technologyreview.com/2023/11/27/1083894/the-download-openai-q-hype-x-financial-woes/
Published Date: Mon, 27 Nov 2023 13:11:00 +0000

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The Download: OpenAI’s wild year, and tech’s cult of personality

This is today’s edition of The Download our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Inside OpenAI’s wild year

Few companies can say they’ve had more of a rollercoaster year than OpenAI. At the beginning of 2023, the world’s hottest AI startup was riding high on the success of its ChatGPT chatbot. Now, it’s dusting itself off from an attempted coup which saw Sam Altman ousted and reinstated as the company’s CEO within a few short days.

Our AI experts have been following OpenAI’s every move throughout the year, often with exclusive access to the people building the revolutionary products and systems. Check out just some of the highlights from the past year—and what we think is coming next.

+ ChatGPT is everywhere. Here’s where it came from. While ChatGPT may have looked like an overnight sensation, it was actually built on decades of research.

+ Back in April, our senior AI editor Will Douglas Heaven had exclusive conversations with four OpenAI insiders to learn more about how they built the viral chatbot.

+ One of the year’s hottest topics was how ChatGPT was already changing education—from cutting corners for science homework, to writing entire theses. But some teachers believe that generative AI could actually make learning better. (Bonus: check out high school senior Rohan Mehta’s robust defense of ChatGPT in the classroom)

+ OpenAI’s hunger for data is coming back to bite it. The company’s AI services may be breaking data protection laws, and there is no resolution in sight. Read the full story.

+ Ilya Sutskever, OpenAI’s chief scientist, was one of the key executives to turn against and try to overthrow CEO Sam Altman in the recent OpenAI revolt. When Will Douglas Heaven met him earlier this year, Sutskever told him about his new priority—figuring out how to stop an artificial superintelligence from going rogue. Read the full story.

+ What’s next for OpenAI. Read our timeline of how the recent drama unfolded, and what it means for the AI industry at large. Read the full story.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 X and OpenAI are cults of personality
While both companies claim to be reshaping the world, they each really answer to one man. (WP $)
We shouldn’t miss the chance to hold OpenAI accountable. (FT $)
Wall Street still sees an opportunity to make money, though. (Motherboard)
AI is a real capitalist’s game these days. (NYT $)

2 Pro-China foreign influencers are Beijing’s latest propaganda tool
Vloggers based in the US are doing the Chinese government’s job for them. (FT $)
China’s young workers are embracing the digital nomad dream. (Reuters)

3 No, AI isn’t human
It’s important to keep reminding ourselves that no matter how convincingly its systems mimic us, they aren’t rational beings. (Vox)
We’re living in the age of uncensored AI. (The Atlantic $)
AI just beat a human test for creativity. What does that even mean? (MIT Technology Review)

4 Australia doesn’t exist
That’s according to Microsoft’s Bing search results, fueled by conspiracy theories. (The Guardian)

5 India’s powerful influencers could sway its elections 
Marketing firms are racing to hire the social media personalities with the biggest followings. (Wired $)

6 Keep an eye on these retail bots this Black Friday
Some are a lot more helpful than others. (WSJ $)

7 Crypto pig butchering schemes are a billion-dollar industry 
But we still know next to nothing about the criminals orchestrating them. (Reuters)
Crypto’s biggest beasts are falling, one by one. (NY Mag $)
The involuntary criminals behind pig-butchering scams. (MIT Technology Review)

8 Social media these days is seriously depressing
Negative news doesn’t just affect us—it changes society, too. (Wired $)
It’s not just you—Zoom meetings really are exhausting. (IEEE Spectrum)
How to log off. (MIT Technology Review)

9 Your messy bedroom’s days are numbered
This laundry-grasping robot is ready to pick up the slack—and stinky socks. (New Scientist $)

10 It’s time to read Napoleon’s sprawling Wikipedia page
Because Ridley Scott, his latest biographer, certainly won’t have. (Slate $)
The new biopic is a surprising lol-fest. (The Atlantic $)

Quote of the day

“When Sam didn’t have a home, Microsoft gave him one without hesitation — and when the whole company didn’t have a home, Microsoft gave them one.”

—Barry Briggs, a former Microsoft executive, tells the Financial Times how Satya Nadella’s savvy handling of the OpenAI drama is likely to further cement the relationship between the two companies.

The big story

California’s coming offshore wind boom faces big engineering hurdles

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By: Rhiannon Williams
Title: The Download: OpenAI’s wild year, and tech’s cult of personality
Sourced From: www.technologyreview.com/2023/11/24/1083869/the-download-openais-wild-year-and-techs-cult-of-personality/
Published Date: Fri, 24 Nov 2023 13:10:00 +0000

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