Category Archives: Artificial Intelligence

Fujifilm SonoSite CEO Richard Fabian holds up the SonoSite 180, the companys first mobile ultrasound device that debuted in 1998, during the Life Science Washington Summit on Oct. 25, 2019. (GeekWire Photo / James Thorne)

Decades of technological advances have led to a revolution in ultrasound machines that has given rise to modern devices that weigh less than a pound and can display images on smartphones. But they still require an expert to make sense of the resulting images.

Its not as easy as it looks, said Richard Fabian, CEO of Fujifilm SonoSite, a pioneer of ultrasound technologies. A slight movement of your hand means all the difference in the world.

Thats why SonoSite is focused on a future in which artificial intelligence helps healthcare workers to make sense of ultrasounds in real-time. The idea is that computers can be trained to identify and label critical pieces of a medical image to help clinicians get answers without the need for specially-trained radiologists.

Using AI you can really quickly interpret whats going on. And the focus is on accuracy, its on confidence, and its on expanding ultrasound users, Fabian said during a talk at Life Science Washingtons annual summit in Bellevue, Wash. on Friday.

Bothell, Wash.-based SonoSite recently partnered with the Allen Institute for Artificial Intelligence (AI2) in Seattle on an effort to train AI to interpret ultrasound images. To train the models, SonoSite is using large quantities of clinical data that it gathered with the help of Partners HealthCare, a hospital in Boston.

Artificial intelligence has shown promise in interpreting medical imaging to diagnose diseases like early-stage lung cancer,breast cancerandcervical cancer. The advancements have drawn tech leaders including Google and Microsoft, who hope their AI and cloud capabilities can one day be an essential element of healthcare diagnostics.

SonoSite was initially launched with the idea of creating portable ultrasounds for the military. Its lightweight units are widely used by healthcare teams in both low-resource settings and emergency rooms.

Ultrasound imaging is significantly more affordable and portable than X-ray imaging, CT scans or PET scans, without the risk of radiation exposure. While the images it provides are not as clear, researchers think deep learning can make up some of that difference.

AI2 researchers are in the process of training deep learning models on ultrasound images in which the veins and arteries have been labeled by sonographers. One application of the AI-powered ultrasound would be to help clinicians find veins much faster and more accurately.

Fabian also gave the example of AI models labeling things such as organs and fluid build-ups inside the body, which could inform care decisions without the need for specialists. He thinks that future ultrasounds could deliver medical insights without ever displaying an image.

If ultrasound becomes cheap enough, it could become a patch [that gives] you the information that you need, said Fabian.

The rest is here:

Why Fujifilm SonoSite is betting the future of ultrasound on artificial intelligence - GeekWire

From self-driving cars to Amazons Alexa, artificial intelligence is already here.

But some fear AI could be used to create killer robots.

Nobel Peace laureate Jody Williams is helping to lead a campaign for a new international treaty to prohibit lethal autonomous weapons, which select targets to fire without consultation from a human being.

Williams said at a news conference on Monday that killer robots "are crossing a moral and ethical Rubicon and should not be allowed to exist and be used in combat or in any other way".

While more and more governments are heavily investing in AI for military purposes the Pentagon alone has pledged up to two billion dollars on AI research Ben Goertzel, the man behind Sophia the Robot's brain, told Euronews that he hopes He hopes we use it for broadly beneficial applications like health, education.

I have no illusion I'm going to stop the militaries of the world from developing autonomous weapons systems," he said.

"I prefer to focus my own energy on more obviously broadly beneficial AI applications.

"So my hope is that you know more and more of the human smarts in the AI field and of the AI smarts (boffins) will go to discovering new things, educating kids and curing disease, he said.

I hope we don't end up with a situation where most of the AI in the world is going into something like a ton of autonomous weapons although I'm sure that's going to be their human society being what it is.

However, the AI expert pointed out that, in his view, advertising and surveillance rather than autonomous weaponry have been driving more of the AI research field.

Though, in his ideal world, AI would only be used in the "creative fields" such as science, maths, and arts.

Ultimately for Goertzel, AI should be about helping rather than destroying.

"What I think is more important is just to put more energy enthusiasm creativity and resources behind the more obviously beneficial applications and then you're sort of you're shifting where the centre of gravity of the human-AI interaction is."

In mid-November, the parties to the Convention on Conventional Weapons will be meeting in Geneva and they could agree to start negotiations on banning lethal weapons.

Original post:

Use artificial intelligence to create rather than to kill, says AI pioneer - Euronews

Aegis AI, an artificial intelligence startup that builds software which employs computer vision to automatically detect weapons in security camera feeds,today announced that its rebranding as Actuate and launched new AI threat-detection features.

Actuate was founded in early 2018 by University of Chicago MBAs Sonny Tai and Ben Ziomek. Tai is a former Marine Corps captain who spent his formative years in Johannesburg, South Africa, where gun violence rates are some of the highest in the world, while Ziomek brings deep data science and AI expertise gained from his time as a program manager at Microsoft.

The New York City-based Aegis Systems is a venture capital-backed AI startup that provides computer vision software to turn any security camera into a threat-detecting smart camera.Aegis AI system automatically detects firearms in existing security camera feeds, providing early warning and dramatically improving law enforcement response.

After careful analysis of the companys market positioning, Actuate leadership decided to adopt the new brand name in alignment with its new features, which expand the firms offerings beyond gun detection, the company said in a press release.

The new features include intruder- and threat-detection AI solutions. The Actuate system can now alert customers to unauthorized entry to customer facilities and catch individuals acting in a threatening manner even before weapons are fully visible.

Our new intruder- and threat-detection features help make our customers safer, said Ben Ziomek, Actuate chief product officer. Were excited to offer them to the market as we debut a new brand that highlights our expanded scope.

Actuate is also launching new, vertical-specific marketing content that targets healthcare, education, corporate, and public-sector customers as part of the rebrand.

The new Actuate logo modernizes the brand while maintaining the key colors and look-and-feel that defined the Aegis brand. It moves the brand from a focus on its defensive posture to a more general, enterprise-ready brand that opens the door to Actuates use as a building-management platform.

This rebrand recognizes that weve grown as a company, said Tai, Actuates chief executive officer. Im thrilled to announce new intruder- and threat-detection features, which we feel strongly contribute to our central goal of making society safer.

Actuate is an AI company that builds computer vision software to turn any security camera into an intruder- and threat-detecting smart camera, dramatically cutting the time it takes for law enforcement to respond to gun violence. Former Marine Sonny Tai, CEO of Aegis AI, and Ben Ziomek, CPO of Aegis AI, co-founded the company with the mission of addressing Americas gun violence epidemic. The product features include alerting customers to unauthorized entry to customer facilities, as well as catching individuals acting in a threatening manner even before weapons are fully visible.

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Artificial intelligence startup Aegis AI rebrands as Actuate; launches new intruder-and-threat-detection AI solutions to keep the society safer from...

The year is 1989 and were introduced to the World Wide Web. The Berlin Wall is coming down. The Exxon Valdez is spilling oil in Prince William Sound, Alaska. Students are calling for democracy and free speech in Tiananmen Square. Crockett and Tubbs are clearing the mean streets of Miami. A future pop star by the name of Taylor Swift is born. This all occurred 30 years ago, around the same time asif not more recently thana number of government systems were put into place.

Fast forward to 2019 and consider all the disruption that emerging technology is presenting to the federal government. Blockchain, quantum computing, internet of things, robotics, 5G the list goes on. What does this mean? When you consider the capabilities of these new technologies and 30, 40 or 50-year-old legacy systems, agencies are generating large volumes of records, information and data in multiple formatsphysical and digitalthat must be leveraged and stored effectively.

No matter the format, all this information is part of a lifecycle: Agencies create it, use it, store it and destroy it. Besides the sheer volume of information, this lifecycle process is no different now than 30 years ago. The question is, how can agencies better manage that lifecycle? And, what can they put into place to glean insights from the information wherever it is within that lifecycle?

Records Managers Can Help

In light of all this informationand pressing National Archives and Records Administration electronic records deadlinesgovernment records managers can help by:

1. Transforming to a New Way of Working

Records managers need to manage information in new ways. Many agencies today struggle with the efficiency of their records and information management (RIM) programs, requiring an investment in capital and resources. Agencies should move to a more optimized IT environment consisting of colocation and cloud services; automated business processes; and outsourcing of non-core processes. This will allow agencies to repurpose their space, reallocate their resources, and achieve a new level of digital maturity.

2. Mitigating Risk

The increase in the volume and variety of information that agencies are experiencing also exposes them to additional risk either from breach, cyberattack or loss. Agencies must mitigate this risk by not only securing where that information resides and how its accessed but also setting and enforcing retention policies, enabling them to know what and when they can defensibly destroy. This also better prepares them for audits or other compliance activities because they know what they have, where it resides and how long they must keep it.

3. Extracting Value

Lastly, by understanding the value of the information, agencies can make better decisions to drive their mission forward. Sixty percent of records, according to the Association for Information and Image Management, are unstructured, meaning that they are providing little value. And, it is estimated that organizations use only 5 to 10% of their overall data. An example of a way for agencies to extract value is to access information that may be available on old media that can be recovered and restored, then harness the power of the information to gain the insights they need to improve operations. Another example includes using AI to extract value out of information used to feed a current workflow, such as being able to pull unstructured data from forms or records that would otherwise require a manual process.

Artificial intelligence capabilities can be the driver behind this third area.

AI and RIM

Incorporating AI into the information lifecycle management function enables agencies to classify and extract information once, then reuse downstream; seamlessly integrate content typesfrom physical to digital; derive actionable insight from dark data (information collected and stored, but never used); as well as ensure the information is managed according to policy.

Agencies should consider using AI with machine-learning capabilities to automatically classify, extract and enrich physical and digital content. ML-based classification of an agencys physical (paper, tape) and digital (application-generated, human-generated) information adds structure, context and metadata to information to make it more predictable and usable. The resulting enriched content can then enable enhanced automation in terms of governance and workflow across the agency.

This can be accomplished by:

Ultimately, incorporating AI with a comprehensive information lifecycle management approach allows agencies to ingest multiple document formats into a single system, apply ML algorithms at the appropriate point, add or replace those algorithms as necessary, and offer deeper insights to achieve greater efficiencies and reduced risks.

Government agencies are generating significant amounts of information and records in both physical and digital formats. In order to enable agencies to better leverage all this information to support the mission, they need to consider an overall information lifecycle management approach that includes comprehensive AI and ML capabilities.

Sue Trombley is the managing director of global engagement for Iron Mountain.

Excerpt from:

Artificial Intelligence and the Information Lifecycle - Nextgov

KT's DL (Deep Learning) based AI module has been implemented and tested on WeDo's RAID FMS system. This AI module, trained with KT Big Data, has showed strong results for fraud detection and prevention, and proved to be highly efficient and effective for a number of fraud use cases, with very high accuracy. KT and WeDo plan to supply the AI-IRSF (AI based International Revenue Share Fraud) module with the RAID platform to CSPs (Communication Service Providers) by the end of the year.

AI-IRSF is an AI system that prevents a fraud that involves hacking of IP-PBX (IP telephony exchange) to generate illegal calls to international numbers.

With this Cooperation Agreement, KT will develop and supply more AI based FMS modules to integrate with WeDo's Fraud and Risk Management system. Additional AI based modules will also run on the WeDo's system, and the modular capability of RAID will allow CSPs to choose from different fraud detection models for their market similar to how one chooses applications from a smartphone app store. The open architecture of RAID will also allow other CSPs to develop their own models as well.

WeDo Technologies is part of the Mobileum group, a leading enterprise software and analytics company in roaming, security, fraud and risk management serving more than 700 telecommunication providers in more than 180 countries.

According to IDC, the global AI application market will rapidly expand from US $15 billion (2017) to US $56 billion in 2020 with CAGR of 55%.

KT plans to apply this AI technology with KT group's financial subsidiary BC Card and Korea's No.1 caller info app "KT WhoWho" for safe financial operation and mobile communication.

KT AI, that has been trained with KT group's extensive Big Data, will secure safety and efficiency of telecommunication FMS. Furthermore, KT will extend its AI ability to global financial FDP (Fraud Detection and Prevention) market.

Global AI-FDP which had the market size of US $1.4 billion (about 9% share of the total market) in 2017, is expected to grow to US $5 billion in 2020.

Young Woo Kim(KT's Senior Vice President, Head of Global Business Development Unit) said: "Within the Cooperation Agreement with WeDo, KT's AI and Big Data technologies combined with WeDo's Global FMS Market ability will become the very successful first step toward global AI market advancement."

Rui Paiva(WeDo Technologies CEO and Mobileum Chief of Revenue Assurance & Fraud Management Business Unit & CMSO) said: "This Cooperation Agreement with KT, is a landmark development for the fraud management market, in a path to provide deep AI fraud prevention capabilities, successfully tested with real life data, to the wider community."

About WeDo Technologies a Mobileum company

WeDo Technologies, founded in 2001, is part of the Mobileum group, a leading enterprise software and analytics company in roaming, security, fraud and risk management serving more than 700 telecommunication providers in more than 180 countries.

Mobileum delivers analytics solutions that generate and protect revenue, reduce direct and indirect costs, and accelerate digital transformation for Communication Service Providers (CSP). Mobileum focus on key CSP domains, including roaming and interconnect, counter fraud and security, revenue assurance, data monetization and digital transformation. Mobileum's success is built upon its unique Active Intelligence platform, which logically combines analytics, engagement, and action technology, with deep network insights and easy CSP system integration to deliver seamless end-to-end solutions. In a recent independent third-party survey of global mobile operators, Mobileum was voted as #1 in Innovation for a group of 183 suppliers.

WeDo Technologies innovative risk management solutions analyze large quantities of data, enabling monitoring and control of enterprise wide processes to ensure revenue protection and risk mitigation. In addition, its data insights empower business management solutions that accelerate automation and optimization across the digital enterprise, and enable efficient business processes such as incentive compensation, collections, and wholesale roaming management

Know more at http://www.wedotechnologies.com and http://www.mobileum.com, and follow @MobileumInc on Twitter

Mobileum is based in California's Silicon Valley, with offices in Argentina, Brazil, Egypt, Hong Kong, India, Jordan, Portugal, Malaysia, Mexico, Singapore, United Arab Emirates, United Kingdom and Uruguay.

Useful Contacts: Phone: +351 962 018 267

Public Relations and Corporate Communications Sara Machadosara.machado@wedotechnologies.com

Analyst Relations and Product Marketing Carlos Marquescarlos.marques@wedotechnologies.com

SOURCE WeDo Technologies - a Mobileum Company

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KT and WeDo Technologies Collaborate on Using Artificial Intelligence to Detect Fraud - PRNewswire

From startups to large firms--everyone is opting for AI-powered digital marketing tools to enhance campaign planning & decision making

September21, 20195 min read

Opinions expressed by Entrepreneur contributors are their own.

You're reading Entrepreneur India, an international franchise of Entrepreneur Media.

Artificial Intelligence (AI) is no longer the next big thing, it is now a big thing now in digital marketing. All digital marketing operations are now affected by AI-powered tools. From startups to large firms are opting for AI-powered digital marketing tools to enhance campaign planning & decision making.

AI-based tools are now a flourishing market, with a drastic change in demand. According to most of the digital marketers AI enhancing all the areas where the predictive analysis, decision making & automation efforts required.

How AI is adding value to digital marketers life?

Digital marketers are trying hard to leverage AI for strategic planning and campaign decision making. Most of them found AI helpful and enhancing their productivity and reducing their efforts. AI-powered analytics tools provide better insights for campaign management, budget planning, & ROI analysis. AI can gather the insights from a truckload of unstructured and structured data sources in a fraction of sec.

Related:The Real Reason Sales and Marketing Teams Use AI

All the human interactions with a business affect the digital marketing strategy and business revenue.

Reportedly, brands who have recently adopted AI for marketing strategy, predict a 37 percent reduction in costs along with a 39 percent increase in revenue figures on an average by the end of 2020 alone.

AI-powered Recommendation Engine to Understand the Customers

Artificial intelligence tools help digital marketers to understand customer behavior and make the right recommendations at the right time. A tool with the millions of predefined conditions knows how customers react to a particular situation, ad copy, videos or any other touchpoint. While humans cant assess the large set of data better than a machine in a limited timeframe.

Related:How AI Is Driving Marketing Automation

You can collect the insights on your fingertips with the help of AI. Where to find an audience? how to interact with them? What to send them? How to send them? What is the right time to connect? When to send a follow-up? All these answers lie in the AI-powered digital marketing platforms.

With a smart analysis pattern AI, tools can make better suggestions and help in decision making. A personalized content recommendation to the right audience at the right time guarantees the success of any campaign.

Digital marketers are really getting pushed harder to demonstrate the success of content and campaigns. With AI tools utilization of potential data is very easy and effective.

According to a2019 studyby Forrester and Albert, only 26% of marketers are making use of autonomous AI, while 74% take a more manual approach with assistance from AI.

AI technology evolving every aspect of digital marketing to name a few audience targeting, audience interest analysis, web optimization, smart content writing and recommendation, advanced tracking and reporting and more.

How AI will play major roles in digital marketing solutions:

Customer data management

Customer behavior analysis and customer experience analysis

Predictive analysis

Trend analysis for campaign planning

Smart pattern analysis

Marketing automation

Real-time data analysis and decision making

Content marketing

Voice search technology

Programmatic advertising

Native advertising

AI in digital marketing is poised to reach a global market of USD 21 billion by 2023, growing at a steady Compound Annual Growth Rate (CAGR) of 26%.

How to prepare for AI-based platforms? How to include them in your digital marketing strategy?

Digital marketing technology platforms are evolving with great pace and it needs some specific set of skills. If you want to opt for smart marketing technology you should start using AI-powered tools from a small scale and increase the limits as you grow. A roadmap is required to stay ahead from the crowd, few tips to prepare for that:

Analyze the actual impact of AI on your digital marketing operations not all the AI tools are helpful for you. You should increase your basic AI knowledge to understand how the tools could make an impact on your current campaigns and reporting areas. You can go for Udemy courses, or YouTube tutorials or Free online courses available.

Evaluate different AI software to employ for your digital marketing campaigns There is a wide range of tools available in the market today for each and every marketing activity. You should evaluate the potential platforms to boost your campaign performance. Go for the demos, product documentation and webinars to know about the tools.

Follow the leading companys case studies Read the case studies form the organization who already implemented the AI-tools in their digital marketing campaigns and showed significant results.

Be creative, be experimental See how you can incorporate the AI tools and your current campaigns, how you can try something new to run experiments to enhance the campaign performance. Being creative is a human-thing, Leverage the power of it!

Go for industry-specific use-cases - To understand the effectiveness of these AI tools, you should explore the industry-specific use-cases. Learn how they implemented the strategy and what was the outcome, how they executed the strategy.

Try new tools every day Take free demos, trials of the tools and explore their potential, leverage your small marketing activities with the help of these tools, once you understand the logic behind it, and outcome rate, you can implement a right digital marketing strategy.

Required some technical skills too AI-based tools require some technical knowledge to integrate with your digital marketing operations. So, be ready or consult with the technical team to provide the necessary support. In-house competency is much needed.

Connect with agencies who already built a strategy with AI Some creative agencies already employed the advanced tools to run the successful digital marketing campaigns. Connect with them, partner with them to gain access to the insights.

AI has a remarkable impact on all the areas of digital marketing and will keep growing in the future. The future of digital marketing is here, the faster you learn the faster you grow. The days are gone when digital marketers run the data and find the insight and other team works on the campaign based on the insights. Things are moving with great pace in digital marketing space. The early adopters will win the game!

Excerpt from:

How Artificial Intelligence is Changing the Landscape of Digital Marketing - Entrepreneur

Maria Bartiromo has been covering business news for 30 years, and shes got her eye on the next big wave: artificial intelligence.

The Fox Business Network anchor, who recently re-signed with the network for a multiyear deal, is releasing an hour-long investigative documentary about artificial intelligence. The segment, which has been in the works for a year now, includes interviews with chief executive officers of major companies including IBM IBM, +0.13% and Ford. F, -0.11%. Fox News parent company Fox Corp FOXA, +0.00% was previously owned by MarketWatch parent News Corp NWS, +0.49%.

Artificial intelligence isnt just making demands to Siri on Apples iPhones, AAPL, +0.45% or telling your Google GOOG, +0.33% email inbox to identify spam. Eventually, people will be talking about artificial general intelligence, which is machine learning that is, the computer deciding on its own what is or isnt relevant to a persons request based on its past experiences. This technology has the potential to create entirely new jobs, eliminate other ones and also save lives, Bartiromo said, and the key for Americans to succeed in the face of this new technology is to understand where and how it will be implemented.

See: Artificial intelligence is revolutionizing the workplace, but its also dominated by men

The documentary will air on Sunday at 8 p.m. EST on Fox News FOX, +0.31%, and is broken into six parts. Bartiromo spoke with MarketWatch about how artificial intelligence will affect future workers and retirement, as well as her own plans.

MarketWatch: What do you see as the biggest impact of artificial intelligence on businesses? Is there a certain sector or facet of the workplace that will be most affected?

Maria Bartiromo: Artificial intelligence is powered by data so any job that involves a lot of data will be impacted first. So lets say youre a mortgage broker, or the person who looks at the eligibility of someone to see if theyre going to get a mortgage. You have to go through large sets of data, which a machine can do fast.

I think the industry that will be most impacted, in a good way, is health care. The machine can look at a radiology report, a mammogram, an MRI it can look at a million eyes and understand which one of those eyes is diseased. Or a million skins and see really simply, easily and quickly what the specifics of that skin is and what the propensity is for it to have cancerous cells. Thats what IBMs Watson is doing in hospitals theyre using AI in very big ways to understand routines and what these reports might show. It wont replace a doctor, but it will just be able to let the doctor do more. When you go to a doctor and you say you have a headache or stomach ache, the first thing the doctor does is eliminate things. That takes time. A computer can do that very quickly. I think it will be lifesaving.

But while it can be lifesaving, it can also be job killing in other industries. I think when you look at those industries, thats when the worry comes in and really sparks a debate in business about how to unleash artificial intelligence in an ethical way.

MW: People seem to think AI will replace older workers. What do you think?

Bartiromo: Not necessarily. It is really about a persons savvy with technology. You can be an older person and be really good at technology, you dont have to be a young person to understand how technology impacts things. So its really about how savvy you are with these changes and how to keep up with this skill set, or you need to be trained. We have seen technology replace jobs our entire careers. We didnt know what jobs would be available in 1998 with the dot-com boom. I remember covering this with a front-row seat in the 1990s when Amazon AMZN, -0.49% went public. We didnt know what the technology meant, that drones might replace delivery people or shopping was going to move online as opposed to brick and mortar. There will be jobs, but the scary thing is we dont know about them yet. It is not targeted toward older people but it is targeted toward white collar.

Read: How AI is catching people who cheat on their diets, job searches and college work

MW: Will AI impact retirement, and if so, how?

Bartiromo: Work is shifting. I dont know how many people you know who retire at 65 and say all right, Im going to go hit the hammock. I am certainly not going to do that. People may retire but they just retire from a job they had for 30 years and then theyll do something else. I am a firm believer of never retire and the reason I say that is because you have to constantly keep your mind going and have to constantly challenge your mind because if you dont use it, youll lose it.

MW: What does retirement mean to you? What do you envision retirement even being for yourself?

Bartiromo: I dont know because Im nowhere near it. I know in my frame of mind right now, Im not going to go hit the hammock. I am a curious person. I love work, I love learning new things and I have an open mind to new ideas. So I doubt when I am faced with that prospect that Im just going to stop working. Im going to do something to enrich my mind. I have no expectations that when I retire from my job Im going to rest and relax. Its just not who I am.

MW: What advice do you have for someone starting to work, especially if there will be more technological advancements in the workplace?

Bartiromo: I would say dont write this off. Understand what you dont know and make sure to bone up on technology if you are in an industry that is data-heavy. Make sure you understand that in the future, machines will be able to handle that so arm yourself with the right training to thrive in the profession. Its really important not to say I dont get that, Im lame when it comes to technology thats a cop out and it will hurt you in the future. Make sure to improve on the skills you need regarding technology and make sure you know the systems.

MW: What would you say are some of the biggest misconceptions of AI in the workplace?

Bartiromo: One misconception is that you have to be afraid of it. You have people like Elon Musk (the chief executive officer of Tesla TSLA, +0.25% ) who say it is dangerous but the reality is it is happening. Technology has always impacted jobs. Dont be afraid of it, understand it and try to educate yourself. Understand what parts of your routine are vulnerable to being replaced.

Another misconception is that when we talk about AI we are talking about Siri and your phone or the Echo home device. Yeah, that is artificial intelligence, but it is more about machine learning, so when I say Siri, put on yoga music, and it learns what yoga music is, that is machine learning. When you talk about artificial general intelligence, where the computer is mimicking the brain to a tee that could be changing the way you live and work.

MW: In your career reporting on business news, how have you seen the emergence of AI reported? How have people been talking about it and how has the perception of AI changed in those years?

Bartiromo: I have been covering business news for 30 years. I have had a front-row seat and seen many cycles and many different innovations. When I first got into business in 1989 at CNN, we were in the middle of the individual investor revolution, where individuals were hungry for information and wanted to arm themselves with information because they thought they could make their own investment decisions. So what happened then we had a whole industry swarmed and discount brokerage firms like E*Trade, ETFC, -0.13% Ameritrade (now TD Ameritrade) AMTD, -0.43% and Schwab SCHW, -0.47% of course thrived in that. Information was peoples currently and that was the power for them to get ahead in their lives.

Also see: The rise of artificial intelligence comes with rising needs for power

From there, we saw the cycle and euphoria of the dot-com boom, so people were investing in things with a .com at the end of it because why? I dont know why. There were no revenue or earnings but the stock was trading at an enormous valuation. It was just mania where we thought we were on the doorstep of a new revolution.

Back then, a lot of companies said they didnt have a dot-com site and they didnt need to compete with Amazon. We know now you did need to compete with Amazon because if you werent online, you took a back seat. We are seeing companies look at AI and start adopting it slowly. We will see companies adopt it in a huge way because theyre going to realize they have to do this to be competitive. The first adopters will be enterprise, so companies, not the consumer, and it will be in your jobs.

See the original post:

Maria Bartiromo talks artificial intelligence, the dot-com crash and why shell never retire - MarketWatch

Businesses still dont have a clear understanding of what to expect when it comes to the ROI of AI. Many believe that AI is just like any other software solution: the returns should, in theory, be immediate. But this is not the case. In addition, business leaders are often duped into thinking the path to ROI is a lot smoother than it is when it comes to AI because AI vendors tend to exaggerate the results their software generates.

In reality, identifying a metric to reliably measure the impact AI is having at a business is very hard.

In this article, we delve deeper into how business leaders should think about identifying ROI metrics that might help them understand the return they could generate from AI projects. To do this, we explore insights from interviews with three experts who were on our AI in Industry podcast this past month.

Special thanks to our three interviewees:

You can listen to our full playlist of episodes in our AI ROI playlist from the AI in Industry podcast. This article is based in large part on all three of these interviews:

Subscribe to the AI in Industry podcast wherever you get your podcasts:

We begin our analysis with a discussion of how to measure the ROI of AI.

AI projects inherently involve a level of uncertainty and experimentation before they can be deemed successful. In a small number of AI use-cases, identifying a measurable metric for projected returns may be relatively simple. For instance, in predictive maintenance applications for the manufacturing sector, businesses can link the returns directly to a reduction in maintenance costs or reduction in machinery downtimes.

But in other applications, such as improving customer experiences in banking, identifying a small number of reliable metrics to measure success is far more challenging.

Unless businesses have a clear understanding of the returns, they stand to risk losing on their AI investments. One way to ensure an AI project has a measurable metric is to choose a specific business problem where there already exists a non-AI solution and results are being measured and tracked.

Jan Kautz, VP of Learning and Perception Research at NVIDIA, who we interviewed for our previous podcast series on getting started with AI, seemed to agree that developing an AI solution for an existing business problem might be easier when it comes to measuring success rather than developing a completely new AI use-case with no precedent:

The danger of doing something completely new in AI is that you dont actually know if what you are doing is actually correct because you have nothing to compare it to. I would suggest banks to pick an area where they already have an existing system in place so that you can compare what the results of the AI system are and know if you are at least getting better results than the existing system

Business leaders also need to understand that in order to deploy an AI project across an organization, they not only require data scientists, but also data engineers. Data scientists are those that develop machine learning algorithms for a particular capability.

Data engineers usually undertake the task of implementing the solution across the enterprise. This might involve identifying if the existing data infrastructure is set up in a sustainable way that will allow AI systems to function smoothly over time and across the organization or that the devops process is capable of sustaining AI projects.

Narayanan believes most successful AI projects that can show positive results will involve data scientists working in collaboration with data engineers. Input from these employees is critical to understanding what a measurable metric of return might because they have the deepest understanding of what the AI system can do.

But these employees usually lack the insight to connect technical benefits to the overall business gains, which needs to come from the subject-matter experts in the domain into which AI is being applied.

Business leaders need to take into account both these perspectives to truly understand what benefits they are likely to get from their AI projects today. This will also help them accurately analyze what they want these AI benefits to look like in the future and tweak their systems towards that eventuality.

According to Martin, in order to successfully realize returns from AI projects, businesses need to figure out how to test their initial assumptions, experiment with AI systems, and identify use-cases as quickly as possible.

Testing whether these initial pilot projects have been successful means measuring the performance of the AI system in the task that it is being applied to.

Measuring success in these initial projects can even go wrong in ways that are not related to the technical challenges involved with AI. For instance, if a business implements an AI customer service software and only a few users are introduced to it because of ineffective marketing campaigns, measuring the returns of the AI system become even more challenging.

This is because the AI system might have been designed perfectly, but the pilot test might not have been accurately representative of whether any returns gained will actually lead to gains when deployed across the organization.

According to Martin, its critical for business leaders to understand that pilot test projects must not be run at scale across the enterprise. Enacting a large project, such as completely overhauling a fraud detection system at a bank, should only be done after careful analysis of the results from several experimental pilot projects. This is in line with Andrew Ngs advice to shoot for first AI projects with 6-12 month timeframes, not massive multi-year roll-outs.

Leaders need to think about this in phases, where the first step is to identify which small AI projects can potentially help the business gain knowledge about working with data and AI capabilities.

This doesnt mean that the smaller AI projects dont need to result in any success metrics. Rather, it means that in some cases, the pilot that shows the most immediate returns may not be the ideal first step for enterprise-wide adoption given a companys goals and long-term AI strategy. Leaders should focus on AI being a long-term skillset that is attained in incremental steps.

In order to measure a specific return, businesses also need to establish what kind of budgets they need for AI projects.

Unlike simple software automation, where costs are much easier to calculate, predicting the budgetary requirements for AI projects is more complex. Martin added that this was one of the more common AI-related questions that business leaders ask him. He said:

If a business leader is looking to answer questions like how much budget an AI project might require before starting the project, the best advice I can give businesses is to first ask how much budget can they realistically allocate to AI projects and then plan around that figure. AI projects are not easy to budget for because you dont know whats going to work and what is not; it involves a lot of experimentation. A business might not be able to ascertain how many such experiments they might need to run before finding a valuable use-case.

Martin stresses the fact that businesses need to think about AI from a long-term strategic perspective. They will have to make a decision on whether they are an AI company or not. Being an AI company means there will be a period of constant experimentation with uncertain results that could sometimes even take 6 months of experimentation to yield any noticeable results.

A recent article on MIT Sloan Business Review states that new ways of working and new management strategies (what might be called change management) are among the largest factors keeping most AI initiatives from generating ROI. Our own research arrives at the same conclusion.

Theres also no guarantee that an AI project will not go above budget, given the aforementioned uncertainty and experimentation involved. Butthis will give the data science team leaders an idea of how many experiments they might be able to conduct realistically and which ones they might need to prioritize.

One big challenge that many businesses might be grappling with when it comes to AI likely lies in ensuring that every dollar that goes into AI projects sees a significant return. Getting a return as soon as possible is the ideal business scenario.

Narayanan spoke about what misconception business leaders might have about measuring returns from AI :

Most of our knowledge around what AI can do for business stems from well-marketed examples in the news media. We find that most of these use-cases have been business problems that are well defined in nature. For example, we have seen reports of AI software beating the best human chess players or Beating humans in Alpha Go These are problems that have definitive end points. But when it comes to the most common business problems in fortune 500 companies. These do not have definite outcome.

What Narayanan seems to be articulating is that businesses might ask questions such as, Is our next product launch going to succeed? These questions are significantly more open-ended than a board game with definitive results. The term success might mean different things to different people or teams within an organization.

It might be hard to frame a clear question with a definitive answer for business problems. These questions at best might indicate that their problem statements can be extremely hazy and complex.

It might be impossible for any firm to look at a bucket of data and report how much business value might be gained from leveraging that data given the right kind of algorithms. This might be hard to digest for business leaders, but they need to expect uncertainty when it comes to AI.

This is not a traditional business mindset in many industries. According to Narayanan:

This is a cultural shift in the way of thinking of about how data might be critical for AI success. Leaders need to think about how AI can solve a business problem at scale for the enterprise, that is aligned with their business objectives as a whole while being highly sustainable.

In this section, we put forth a list of frameworks that business leaders can follow in order to maximize the possibility of gaining positive returns from their AI projects and effectively measuring it as such.

Traditionally in business, the term ROI usually corresponds to short-term financial gain, often in terms of improved revenue. AI is a broad technology and sticking to this traditional method of defining ROI might not be the best place to start for businesses. For instance, AI might very well be used to potentially increase revenue in an application.

However, AI can also be used to reduce costs, improve customer experience, or increase the productivity of a specific team within the business. The first step to understanding AI ROI might be to associate the returns with any types of positive business outcome, not necessarily financial gains, including leveling up a teams AI-related skillset.

Carmona said that in his experience, there have been several instances in which businesses have needed to invest funds in an AI project as it is being built due to budgetary constraints.

At the same time, business leaders might be looking for immediate returns on their AI investments. According to Carmona, balancing these two factors (uncertainty in AI projects and gaining returns fast) is something business leaders have to figure out before starting AI projects of any kind.

He spoke about a particular framework used by Microsoft (called the Agile AI framework) to find a balance between the two. We detail the steps involved in this framework below with insights from the interview:

Narayanan stated that one of the critical things for business leaders to understand about measuring the returns of AI projects is to first frame the business question that AI is being applied to in a way that is specific.

For instance, leaving aside the technical concerns, businesses first need to ask questions such as Is AI being used to solve a problem in the rate of growth of the organization, or is it being used to improve the efficiency of a business process or to improve customer experiences?

He went on to give an example of a firm that he claimed worked with Fractal Analytics in the past to explain this concept better:

About 18 months before getting into AI projects the client we were working with brought in a visionary leader who said im not supporting any initiative that cant show progress in 6 weeks.. He seemed to enforce this constraint even though he understood that there are a number of initiatives that are transformative long term engagements at enterprise level. This allowed the company to be more rapid defining areas to work and ruthless about what success and progress means and therefore establish a codified approach to measurement.

In a 12 month period they executed 30-40 different initiatives that they called Minimum Viable Proposition (MVPs). They identified 5-6 which had the potential to become transformative at enterprise level and this year they are taking these to deploy on an organizational scale.

According to Narayanan, the client gleaned the following three insights from this process:

See the rest here:

Unlocking the ROI of Artificial Intelligence Key Considerations for Business Leaders - Emerj

The aftermath of an El Nio event in Peru in 2017

By Warren CornwallSep. 18, 2019 , 1:30 PM

The dreaded El Nio strikes the globe every 2 to 7 years. As warm waters in the tropical Pacific Ocean shift eastward and trade winds weaken, the weather pattern ripples through the atmosphere, causing drought in southern Africa, wildfires in South America, and flooding on North Americas Pacific coast. Climate scientists have struggled to predict El Nio events more than 1 year in advance, but artificial intelligence (AI) can now extend forecasts to 18 months, according to a new study.

The work could help people in threatened regions better prepare for droughts and floods, for example by choosing which crops to plant, says William Hsieh, a retired climate scientist in Victoria, Canada, who worked on early El Nio forecasts but who was not involved in the current study. Longer forecasts could have large economic benefits, he says.

Part of the problem with some El Nio forecasts is that they rely on a relatively small set of historical statistics for factors such as ocean temperature. Other forecasts use climate models but struggle to create the detailed pictures of the ocean needed for long-range forecasts.

The new research uses a type of AI called a convolutionalneural network, which is adept at recognizing images. For example, the neural network can be trained to recognize cats in photos by identifying characteristics shared by all cats, such as whiskers and four legs. In this case, researchers trained the neural network on global images of historic sea surface temperatures and deep ocean temperatures to learn how they corresponded to the future emergence of El Nio events.

Such neural networks need a large number of training images before they can identify underlying patterns. To get around the shortage of historic El Nio data, the scientists fed the program re-creations of historic ocean conditions produced by a set of reputable climate models, ones frequently used for study climate change, says the studys lead author, Yoo-Geun Ham, a climate scientist at Chonnam National University in Gwangju, South Korea. As a result, the scientists could show the computer system not just one set of actual historic data, spanning 1871 to 1973, but several thousand simulations of that same data by the climate models.

When tested against real data from 1984 to 2017, the program was able to predict El Nio states as far out as 18 months, the team reports today in Nature. The program was far from perfect: It was only about 74% accurate at predicting El Nio events 1.5 years into the future. But thats still better than best current model, which is only 56% accurate for that time frame, Ham says.

The AI also proved more adept at pinpointing which part of the Pacific would heat up the most. That has real-world implications, because El Nios centered in the eastern Pacific, closer to South America, translate into hotter water temperatures in the northern Pacific and more flood-inducing rain in the Americas, compared with El Nios that are centered farther to the west.

The use of the climate models to create extra training data is a clever way around the shortcomings of other approaches, Hsieh says. It appears enough of an advance that it should be deployed for real forecasts, he says.

But its not clear how much real-world benefit will come from pushing forecasts beyond 1 year, cautions Stephen Zebiak, a climate scientist and El Nio modeling expert at Columbia Universitys International Research Institute for Climate and Society in Palisades, New York. The kind of lead time that is actionable is probably less than a year, because decision-makers are unlikely to take action further in advance, he says.

The researchers have already begun to issue forecasts extending into 2021, and predict a likely La Nia eventEl Nios cooler oppositewhich can bring heavier monsoons and droughts. But major government forecasting agencies are not yet considering the groups predictions. Ham says he and his colleagues are tweaking the model to extend the forecast even further. Meanwhile, he says his team is now working to improve forecasts for another ocean pattern, the Indian Ocean Dipole. That fluctuation in ocean temperatures can influence rain and tropical cyclones in Asia and Australia.

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Artificial intelligence could predict El Nio up to 18 months in advance - Science Magazine

Suzanne Livingston, curator of the recent Barbican Centre exhibition about artificial intelligence, will speak about the impact of technology on cities at Dezeen Day on 30 October.

She will take part in a discussion about future cities, which will explore how urban areas will change in the face of technological, social and environmental pressures.

Livingston curated AI: More than Human, which ran in London from May to August this year and will now tour internationally.

The exhibition explored how AI will affect our lives and featured cutting-edge work by designers including Neri Oxman, Es Devlin, TeamLab and Yuri Suzuki. It explored topics including facial recognition, robotics and how AI can improve urban planning and road safety.

Livingston has a PhD in Philosophy from Warwick University, where she was a founding member of the influential Cybernetic Culture Research Unit (CCRU).

She worked as global principal at branding consultancy Wolff Olins, where she worked on strategy and exhibitions for museums and technology companies.

Now working independently as a consultant, she writes about technology, belief systems, innovation and evolution.

Livingston will be joined on the future cities panel by transportation designer Paul Priestman and experimental architect Rachel Armstrong.

Dezeen Day takes place atBFI Southbankin central London on 30 October. The international conference aims to set the agenda for architecture and design. It will discuss topics including design education, future cities and post-plastic materials.

Speakers includePaola Antonelli,Benjamin Hubert,Dara HuangandPatrik Schumacher. Seeall the speakers that have been announced so far.

Reducedearly bird ticketsplus a limited number of half-price student tickets areon sale now. Buy them using the widget below orclick here to subscribe to the Dezeen Day newsletterfor regular updates.

The illustration is by Rima Sabina Aouf.

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Suzanne Livingston to speak about artificial intelligence at Dezeen Day - Dezeen