Month: September 2019

30 Sep 2019

Human + Machine Collaboration: Work in the Age of Artificial Intelligence

Human and Machine collaboration reimagines processes with AI, letting humans work more like humans and less like robots.

In this age of Artificial Intelligence (AI), we are witnessing a transformation in the way we live, work, and do business. From robots that share our environment and smart homes to supply chains that think and act in real-time, forward-thinking companies are using AI to innovate and expand their business more rapidly than ever. 

Indeed, this is a time of change and change happens fast. Those able to understand that the future includes living, working, co-existing, and collaborating with AI are set to succeed in the coming years. On the other hand, those who neglect the fact that business transformation in the digital age depends on human and machine collaboration will inevitably be left behind.  

Humans and machines can complement each other resulting in increasing productivity. This collaboration could increase revenue by 38 percent by 2022, according to Accenture Research. At least 61 percent of business leaders agree that the intersection of human and machine collaboration is going to help them achieve their strategic priorities faster and more efficiently. 

Human and machine collaboration is paramount for organizations. Having the right mindset for AI means being at ease with the concept of human+machine, leaving the mindset of human Vs. machine behind. Thanks to AI, factories are now requiring a little more humanity; and AI is boosting the value of engineers and manufacturers. 

Business transformation in the era of AI 

The emergence of AI is creating brand new roles and opportunities for humans up and down the value chain. From workers in the assembly line and maintenance specialists to robot engineers and operations managers, AI is regenerating the concept and meaning of work in an industrial setting. 

According to Accenture‘s Paul Daugherty, Chief Technology and Innovation Officer, and H. James Wilson, Managing Director of Information Technology and Business Research, AI is transforming business processes in five ways: 

  • Flexibility: A change from rigid manufacturing processes with automation done in the past by dumb robots to smart individualized production following real-time customer choices brings flexibility to businesses. This is particularly visible in the automotive manufacturing industry where customers can customize their vehicle at the dealership. They can choose everything from dashboard components to the seat leather –or vegan leather– to tire valve caps. For instance, at Stuttgart’s Mercedes-Benz assembly line there are no two vehicles that are the same. 

  • Speed: Speed is super important in many industries, including finance. The detection of credit card fraud on the spot can guarantee a card holder that a transaction will not be approved if fraud was involved, saving time and headaches if this is detected too late. According to Daugherty and Wilson, HSBC Holdings developed an AI-based solution that uses improved speed and accuracy in fraud detection. The solution can monitor millions of transactions on a daily basis seeking subtle pattern that can possibly signal fraud. This type of solution is great for financial institutions. Yet, they need the human collaboration to be continually updated. Without the updates required, soon the algorithms would become useless for combating fraud. Data analysts and financial fraud experts must keep an eye on the software at all times to assure the AI solution is at least one step ahead of criminals. 

  • Scale: In order to accelerate its recruiting evaluation to improve diversity, Unilever adopted an AI-based hiring system that assesses candidate’s body language and personality traits. Using this solution, Unilever was able to broaden its recruiting scale; job applicants doubled to 30,000, and the average time for arriving to a hiring decision decreased to four weeks. The process used to take up to four months before the adoption of the AI system. 

  • Decision Making: There is no secret to the fact that the best decision that people make are based on specific, tailored information received in vast amounts. Using machine learning and AI a huge amount of data can be quickly available at the fingertips of workers on the factory floor, or to service technicians solving problems out in the field. All data previously collected and analyzed brings invaluable information that helps humans solve problems much faster or even prevent such problems before they happen. Take the case of GE and its Predix application. The solution uses machine-learning algorithms to predict when a specific part in a specific machine might fail. Predix alerts workers to potential problems before they become serious. In many cases, GE could save millions of dollars thanks to this technology collaborating with fast human action.

  • Personalization: AI makes possible individual tailoring, on-demand brand experiences at great scale. Music streaming service Pandora, for instance, applies AI algorithms to generate personalized playlists based on preferences in songs, artists, and genres. AI can use data to personalize anything and everything delivering a more enjoyable user experience. AI brings marketing to a new level. 

AI will create new roles and opportunities 

Of course, some roles will come to an end as it has happened in the history of humanity every time there has been a technological revolution. However, the changes toward human and machine collaboration require the creation of new roles and the recruiting of new talent; it is not just a matter of implementing AI technology. We also need to remember that there is no evolution without change. 

 

Robotics and AI will replace some jobs liberating humans for other kinds of tasks, many that do not yet exist as many of today’s positions and jobs did not exist a few decades ago. Since 2000, the United States has lost five million manufacturing jobs. However, Daugherty and Wilson think that things are not as clear cut as they might seem.

In the United States alone, there are going to be needed around 3.4 million more job openings covered in the manufacturing sector. One reason for this is the need to cover the Baby Boomers’ retirement plans.

Source: https://interestingengineering.com/human-machine-collaboration-work-in-the-age-of-artificial-intelligence

29 Sep 2019
International tax regime stressed for growing digital economy

International tax regime stressed for growing digital economy

ISLAMABAD: The emergence of the digital economy has posed an unprecedented challenge to international taxation, and the nexus principle, which requires personal or physical presence as the legal basis for imposing taxes is fundamentally shaken, according to the top official of the United Nations in Asia and the Pacific region.

In a written interview with Dawn, UN Under-Secretary-General and Executive Secretary of the United Nations Economic and Social Commission for Asia and the Pacific (UN-ESCAP), Armida Salsiah Alisjahbana said a strengthened international cooperation is needed to establish an up-to-date international tax regime, preferably on a multilateral basis, to address these challenges and to ensure a fair allocation of tax revenues across country borders.

“The business models of digital economy have facilitated increasing separation of economic activities from physical or personal presence. With the expansion of digital sectors, the friction between the nexus principle for taxation and business models in the digital age is leaving growing shares of the economy untaxed or under-taxed, and this problem is further complicated by digital intangible assets,” Ms Alisjahbana explained.

Developing countries have been advocating for a greater role for the UN system in facilitating broad-based tax cooperation and international tax reforms. ESCAP, as a regional arm of the United Nations, is taking a greater initiative in facilitating regional tax dialogues and cooperation, in close collaboration with broad stakeholders. This role is especially important as Asia-Pacific remains the only developing region without a well-established region-wide tax cooperation body, UN official said.

UN-ESCAP organised last week a conference on productive job creation in the face of economic transformation in the global south.

She said that the exact scale and pace of the digital revolution over the coming years is difficult to precise predict. “We need to be prepared by shifting our focus from ‘digital skills’ to supporting ‘skills for a digital age, essential for a workforce fit for the fourth industrial revolution.”

A shift in thinking in traditional education delivery to life-long learning, and social safety nets will be critical to deal with current and future technological transitions, she said.

Universal Basic Income pilots have been trialed around the world to strengthen social protection systems to protect the workers that are vulnerable to losing their jobs in the technological transition. It is clear that new technologies could lead to the disappearance of some jobs, but they will also support the creation of others, UN-ESCAP official said.

In the area of trade policies, globally, WTO plays a key role in facilitating discussions on trade rules and practices regarding electronic commerce, yet progress under the WTO work programme on electronic commerce has been highly limited due to divided views across countries. Recent years witnessed increasing regional cooperation on electronic commerce, with 69 regional trade agreements signed in 2001-2016.

She said that ESCAP has advocated for a regional solution to enhance electronic commerce, including under its Framework Agreement on Facilitation of Cross-border Paperless Trade in Asia and the Pacific.

Source: https://www.dawn.com/news/1507935/international-tax-regime-stressed-for-growing-digital-economy

28 Sep 2019
You Don't Need a PhD to Join the AI Economy

You Don’t Need a PhD to Join the AI Economy

The AI revolution has arrived. And although the technology is still in its infancy, it promises to radically transform the global economy—impacting human lives, culture, and politics in ways that we can scarcely imagine. A recent article by Forbes Technology Council member Christian Pedersen argued that artificial intelligence will create new opportunities for data scientists, researchers, analysts, and other highly educated technical specialists even as the more easily-automated job functions of low skill workers “fall to the wayside.”

Pedersen’s argument is correct on both counts, but the AI economy is also dependent upon one more ingredient: subject-matter expertise. Participation in the burgeoning AI economy doesn’t require an advanced degree in data science or fluency in the latest programming languages. In fact, many of today’s workers already have the sort of invaluable subject matter expertise that will be essential to helping AI become more sophisticated, efficient, and useful in the years to come. The future of industry will not be one in which humans are replaced by AI, but rather one in which humans and AI work together. That’s because, as it turns out, AI isn’t all that smart without us humans.

How to Train Artificial Intelligence


The AI revolution has arrived. And although the technology is still in its infancy, it promises to radically transform the global economy—impacting human lives, culture, and politics in ways that we can scarcely imagine. A recent article by Forbes Technology Council member Christian Pedersen argued that artificial intelligence will create new opportunities for data scientists, researchers, analysts, and other highly educated technical specialists even as the more easily-automated job functions of low skill workers “fall to the wayside.”

Pedersen’s argument is correct on both counts, but the AI economy is also dependent upon one more ingredient: subject-matter expertise. Participation in the burgeoning AI economy doesn’t require an advanced degree in data science or fluency in the latest programming languages. In fact, many of today’s workers already have the sort of invaluable subject matter expertise that will be essential to helping AI become more sophisticated, efficient, and useful in the years to come. The future of industry will not be one in which humans are replaced by AI, but rather one in which humans and AI work together. That’s because, as it turns out, AI isn’t all that smart without us humans.

How to Train Artificial Intelligence

For decades, we’ve watched as advances in industrial automation have precipitated the gradual decline of low skill job opportunities in manufacturing and other blue collar industries. Now we’re beginning to see that AI has the capacity to replace skilled, white-collar knowledge workers as well. While it’s true that workers must evolve to stay relevant in a job market that will be increasingly shaped by AI and automation, this job market won’t just need technical experts with advanced degrees. AI-powered solutions require much more than just developers, researchers, and analysts to operate effectively. AI needs training.

For example, Volkswagen’s Innovation and Engineering Center California (IECC) recently debuted a new version of the classic 1962 VW Microbus, which was created by AI using generative design. The VW Type 20 Concept features bright orange wheels, side mirror supports, and other components that replace the usual straight lines and bulky forms of traditional car design with unusual, 3D-printed support lattices resembling tree branches or vines. These structures may look strange, but they provide the same level of support as standard components while using significantly less material.

Does this mean that generative design will eventually replace the human designers of the future? Not quite. Like most AI applications, generative design relies on human input to set constraints and handle complex decision making. As IECC Principal Product Designer Erik Glaser notes, “their software figures out a bunch of optimized results—some of which look insane—and then you pick the ones you like.” Even as AI evolves to take on high-level duties like product design, it requires human assistance to create something that humans will actually use.

AI designed for simple tasks like image recognition can be trained on data alone, but AI assigned to more specialized tasks needs input from human experts. Post-doctoral researchers, data scientists, and other AI specialists may be brilliant people with diverse skill sets, but rarely do they have insight into the nuances of product design, the delicacies of customer service, or the subtleties of concierge hospitality. A computer programmer can create AI capable of performing the most general tasks—answering a customer inquiry or checking in a hotel guest, for example—but only the knowledge and experience of subject-matter experts can help virtual agents become truly useful and effective.

Source: https://www.industryweek.com/technology-and-iiot/you-dont-need-phd-join-ai-economy

26 Sep 2019
The Key To Successful AI: Hiding Its Use From People

The Key To Successful AI: Hiding Its Use From People

AI is proving itself superior to human intelligence in an expanding number of fields. That is, except when people know AI is being used.

Yes, because in certain human-centric sectors, the performance of artificial intelligence starts to drop off if people are apprised of the involvement of an intelligent machine. In fact, human resistance would seem to be the achilles heel of artificial intelligence, since for all the recent advances of AI technology this resistance is preventing AI from doing its job in areas where human contact and interaction would normally play a central role.

This message was brought home most recently by a study published in Marketing Science on September 20, titled “The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases.” In it, an international team of researchers found that chatbots used by an unnamed financial services company were as effective in selling products as proficient sales employees, and also four times more effective than inexperienced workers.

The thing is, when customers were informed before any conversation that they would be speaking to a chatbot, the ability of the AI-based assistants to encourage customer purchases fell by a massive 79.7%.

“Our findings show when people don’t know about the use of artificial intelligence (AI) chatbots they are four times more effective at selling products than inexperienced workers, but when customers know the conversational partner is not a human, they are curt and purchase less because they think the bot is less knowledgeable and less empathetic,” said co-author Xueming Luo, a professor in marketing at Temple University.

Similar results have emerged from other studies. Earlier this month, researchers from NYU and Boston University found that patients were less receptive to AI-based healthcare provision and were less likely to trust artificially intelligent healthcare services over human providers. This perhaps isn’t surprising, but it also isn’t particularly rational, since AI has been shown in experiments to be at least equal with human experts in diagnosing medical conditions based on images, for example.

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Likewise, in the context of money, research published by enterprise software firm VMware in early 2019 concluded that only 19% of people in the UK would be happy providing AI with a role in managing their finances. This is despite the fact that there’s already evidence of the superiority of AI-based investment, with data from Eurekahedge revealing that the annualized rate of return for 14 AI-driven hedge funds is 12.74% (as of August 2019), whereas the average rate for the large hedge funds of its “Eurekahedge 50” is only 5.12%. And more broadly, the average annual rate of return for AI funds over the decade from 2010 to 2019 was 13.18%, while the ten-year average for the Eurekahedge 50 was only 5.4% (and the ten-year average for Eurekahedge’s main index of 2,500 funds was 4.9%).

In other words, our in-built prejudice against AI is preventing the technology from being used and applied more regularly, and from reaching its fullest potential. Meanwhile, the Marketing Science suggests one of two ways out of this predicament: either by hiding the use of AI from people entirely, or from working to build trust gradually via incremental exposure to AI.

In some respects, firms engaged in AI are probably already hiding – or at least underplaying – its use of the technology. There is evidence that people are interacting with artificial intelligences without even knowing it, as indicated by a 2017 Pega survey which revealed that, while only 33% of people believe they use AI technology, around 77% actually do so. Likewise, a 2018 BarclayHedge survey indicated that around 56% of hedge funds are relying on artificial intelligence to some degree to inform their investment decisions, even though the vast majority of people are still wary about trusting AI with their finances.

However, while it may end up being effective to quietly ‘force’ AI on the public, it’s not likely to be a viable strategy in the long term. In July, the State of California passed a law requiring companies to ensure that chatbots disclose themselves to consumers, paving the way for similar rules to be passed in other states and possibly at a federal level.

As a result, the tactic of secretly infiltrating chatbots and AI into wider society looks like it’s already endangered. Instead, the AI and tech industry will have to settle for building trust in artificial intelligence over time, by conducting more studies demonstrating AI’s efficacy, by gradually rolling out AI-based services in a piecemeal fashion, and by striving constantly to make AI more explainable to the general public. It’s only by doing this that society will come to permit a more central role for artificial intelligence, and that AI will have a positive effect on us rather than an uncertain and potentially negative one.

Source: https://www.forbes.com/sites/simonchandler/2019/09/26/the-key-to-successful-ai-hiding-its-use-from-people/#191bea112ef7

25 Sep 2019
AI Can Make Cardiac MRI Scans 186 Times Faster to Read

AI Can Make Cardiac MRI Scans 186 Times Faster to Read

Cardiac magnetic resonance imaging (MRI) can be read significantly faster via artificial intelligence, a study says.

When it comes to reading MRI scans, the greatest source of misreadings come from human error.

A new study says the use of artificial intelligence can do away with these errors, all while making the reading time significantly faster than it would be if carried out by a human.

Rapid AI readings

The new research, published in Circulation: Cardiovascular Imaging, describes how analyzing heart function on cardiac MRI scans typically takes about 13 minutes for a human doctor. Using artificial intelligence, the study says, allows for scans to be analyzed in as little as 4 seconds.

“Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated,” said study author Charlotte Manisty, M.D. Ph.D.

“Automated machine learning techniques offer the potential to change this and radically improve efficiency, and we look forward to further research that could validate its superiority to human analysis,” she continued.

Time-saving and life-saving AI

In the UK, where the study was carried out, it is estimated that more than 150,000 cardiac MRI scans are performed on a yearly basis.

Due to the high amount of scans, and how much accumulated time could be saved on all of these, the researchers believe that using AI to read scans has the potential to save 54 clinician-days every year at each UK health center where the scans are carried out.

On top of this, the AI has great potential for eradicating human error in the scan reading process.

“Our dataset of patients with a range of heart diseases who received scans enabled us to demonstrate that the greatest sources of measurement error arise from human factors,” Manisty explains.

“This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘super-human’ — transforming clinical and research measurement precision.”

For their research, the scientists trained a machine learning neural network AI to read cardiac MRI scans with the results of almost 600 patients.

The AI was then tested for precision in comparison with an expert and trainee on 110 separate patient cases from multiple centers.

 

The team of researchers’ findings showed that there was no significant difference in accuracy between the AI and human readings, while those of the AI were carried out much faster.

Source: https://interestingengineering.com/ai-can-make-cardiac-mri-scans-186-times-faster-to-read

24 Sep 2019
Computing and artificial intelligence: Humanistic perspectives from MIT

Computing and artificial intelligence: Humanistic perspectives from MIT

How the humanities, arts, and social science fields can help shape the MIT Schwarzman College of Computing — and benefit from advanced computing.

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines.

To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT’s humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions:  

1) What domain knowledge, perspectives, and methods from your field should be integrated into the new MIT Schwarzman College of Computing, and why?

2) What are some of the meaningful opportunities that advanced computing makes possible in your field? 

As Nobles says in her foreword to the series, “Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being.”

The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences.

Foreword by Melissa Nobles, professor of political science and the Kenan Sahin Dean of the MIT School of Humanities, Arts, and Social Sciences

“The advent of artificial intelligence presents our species with an historic opportunity — disguised as an existential challenge: Can we stay human in the age of AI?  In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action.”

Read more: http://news.mit.edu/2019/computing-and-ai-humanistic-perspectives-0924

23 Sep 2019
The AI arms race spawns new hardware architectures

The AI arms race spawns new hardware architectures

As society turns to artificial intelligence to solve problems across ever more domains, we’re seeing an arms race to create specialized hardware that can run deep learning models at higher speeds and lower power consumption.

Some recent breakthroughs in this race include new chip architectures that perform computations in ways that are fundamentally different from what we’ve seen before. Looking at their capabilities gives us an idea of the kinds of AI applications we could see emerging over the next couple of years.

Neuromorphic chips

Neural networks, composed of thousands and millions of small programs that perform simple calculations to perform complicated tasks such as detecting objects in images or converting speech to text are key to deep learning.

But traditional computers are not optimized for neural network operations. Instead they are composed of one or several powerful central processing units (CPU). Neuromorphic computers use an alternative chip architecture to physically represent neural networks. Neuromorphic chips are composed of many physical artificial neurons that directly correspond to their software counterparts. This make them especially fast at training and running neural networks.

The concept behind neuromorphic computing has existed since the 1980s, but it did not get much attention because neural networks were mostly dismissed as too inefficient. With renewed interest in deep learning and neural networks in the past few years, research on neuromorphic chips has also received new attention.

Read more: https://venturebeat.com/2019/09/21/the-ai-arms-race-spawns-new-hardware-architectures/

 

22 Sep 2019
Artificial Intelligence (AI) creates new possibilities for personalisation this year

Artificial Intelligence (AI) creates new possibilities for personalisation this year

Technology brands expand beyond their core products and turn themselves into a lifestyle

New Delhi: Artificial Intelligence (AI) and cross-industry collaborations are creating new avenues for data collection and offering personalised services to users this year, according to a report.

Among other technology trends that are picking up this year are the convergence of the smart home and healthcare, autonomous vehicles coming for last-mile delivery and data becoming a hot-button geopolitical issue, according to the report titled “14 Trends Shaping Tech” from CB Insights.

“As a more tech-savvy generation ages up, we’ll see the smart home begin acting as a kind of in-home health aide, monitoring senior citizens’ health and well being. We’ll see logistics players experiment with finally moving beyond a human driver,” said the report.

“And we’ll see cross-industry collaborations, whether via ancestry-informed Spotify playlists or limited edition Fortnite game skins,” it added.

In September 2018, Spotify partnered with Ancestry.com to utilise DNA data to create unique playlists for individuals.

Playlists reflect music linked to different ethnicities and regions. A person with ancestral roots in Bengaluru, for example, might see Carnatic violinists and Kannada film songs on their playlists.

DNA data is also informing how we eat. GenoPalate, for example, collects DNA info through saliva samples and analyses physiological components like an individual’s ability to absorb certain vitamins or how fast they can metabolize nutrients.

From there, it matches this information to nutrition analyses that it has conducted on a wide range of food and suggests a personalised diet. It also sells its own meal kits that use this information to map out menus.

“We’ll also see technology brands expand beyond their core products and turn themselves into a lifestyle,” said the report.

For example, as electric vehicle users need to wait for their batteries to charge for anywhere from 30 minutes to two hours, the makers of these vehicles are trying to turn this idle time into an asset.

China’s NioHouse couples charging stations with a host of activities. At the NioHouse, a user can visit the library, drop children off at daycare, co-work, and even visit a nap pod to rest while charging.

Nio has also partnered with fashion designer Hussein Chalayan to launch and sell a fashion line, Nio Extreme.

Tech companies today are also attempting to bridge the gap between academia and the career market.

Companies like the Lambda School and Flatiron School offer courses to train students on exactly the skills they will need to get a job, said the report.

These apprenticeships mostly focus on tech skills like computer science and coding. Training comes with the explicit goal of employment and students only need to pay their tuition once they have landed a job that pays them above a certain range.

Investors are also betting on the rise of digital goods. While these goods cannot be owned in the physical world, they come with clout, and offer personalisation and in-game experiences to otherwise one-size-fits-all characters, the research showed.

Source: https://gulfnews.com/technology/artificial-intelligence-ai-creates-new-possibilities-for-personalisation-this-year-1.1569149228735

21 Sep 2019
To survive, asset managers need to embrace disruptive technologies

To survive, asset managers need to embrace disruptive technologies

FROM THE OUTSIDE, asset management looks like an exciting industry that’s immune to technology — but that’s not true.

Changes in regulations, rising customer expectations, and the growing pressure from new-age competitors are forcing the asset management industry to explore disruptive technologies in order to stay in business.

According to a new study by the Investment Company Institute (ICI), the asset management industry is at a critical juncture in its history — investing in innovation and reinvigorating their products and processes.

In fact, while front-office transformations remain slow, operations executives are aggressively transforming their operating models to achieve greater agility and cost-effectiveness, as they take on the challenges of supporting more complex products and services.

The study revealed that 64 percent of firms surveyed for the report have completed a major operating model change in the past three years to improve operational efficiencies.

Asset managers, many of whom are uncertain about the ability of their operations and technology to support the firm’s objectives, believe they need to alter their strategies at the front-end to focus on driving distribution and creating differentiated products.

“Doing this effectively requires embracing technology and innovation, including investment platform technology and artificial intelligence, for better investment decision-making,” said Accenture Senior MD Michael Spellacy — whose firm collaborated with ICI to create the report.

The study shows that 55 percent of asset management firms reported having a formal initiative in place to evaluate the business and operational potential of new technologies such as the cloud and APIs.

Asset managers join the fintech ecosystem

Given the rapid pace of development in the world of technology, asset managers are also evaluating partnerships with the fintech ecosystem and exploring collaborations with start-ups, accelerators, and incubators.

The study also reveals that middle office functions—including collateral management, data management, derivatives processing, and transaction management—will be the biggest beneficiaries of fintech partnerships.

In the back office, the report found that respondents expect that fintech firms will be able to quickly and successfully help transform expense management, fund accounting, and financial reporting.

Approximately one-third of the firms agreed that at-scale middle office fintech partnerships were common across the industry, suggesting that these partnerships are already delivering results.

According to analysts, however, in order to ensure the success of a fintech partnership, asset managers need to adopt a laser-like focus on delivering bottom-line impact.

“It is vital to avoid the initial focus on ‘shiny objects’ that can result in proofs of concept that lack a clear vision of eventual production and outcomes,” the report pointed out.

Transformation key to long-term success

Technology adoption in the asset management industry isn’t exactly driven by consumer demand for better experiences — but stakeholders do expect more transparency, accountability, and control over their monies.

Further, with margin pressures making profitability difficult, using disruptive technologies might provide new revenue opportunities to asset managers through smarter and more intelligent product and service portfolios.

The survey conducted by ICI suggests that decision-makers in the industry are aware of the changing landscape and are taking action.

“Asset managers are disrupting their legacy operating models and skillsets to reequip their firms to win in a disrupted future.

 

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“Executing this transformation successfully is imperative for long-term success,” concluded the report.

Many asset managers have shifted their strategic focus to the front office and clients and are looking at accelerating their journey to technology in order to boost operational capabilities and acquire/develop top talent in order to support the evolving needs of the front office.

In the near future, partnerships with the fintech ecosystem and customer-driven technology projects are expected to drive the asset management industry into a new space — where digital is weaved into the very fabric of the organization.

Source: https://techwireasia.com/2019/09/to-survive-asset-managers-need-to-embrace-disruptive-technologies/

19 Sep 2019
Artificial Intelligence (AI) Stats News: AI Is Actively Watching You In 75 Countries

AI Is Actively Watching You In 75 Countries

Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the strong state of AI surveillance worldwide, the lack of adherence to common privacy principles in companies’ data privacy statement, the growing adoption of AI by global businesses, and the perception of AI as a major risk by institutional investors.

AI surveillance and the state of data privacy

At least 75 out of 176 countries globally are actively using AI technologies for surveillance purposes, including smart city/safe city platforms (56 countries), facial recognition systems (64 countries), and smart policing (52 countries); technology linked to Chinese companies—particularly Huawei, Hikvision, Dahua, and ZTE—supply AI surveillance technology in 63 countries and U.S. firms’ technology—from IBM, Palantir, and Cisco—is present in 32 countries; 51% of advanced democracies deploy AI surveillance systems [Carnegie Endowment for International Peace AI Global Surveillance (AIGS) Index]

An analysis of 29 variables in 1,200 privacy statements against common themes in three major privacy regulations (the EU’s GDPR, California’s CCPA, and Canada’s PIPEDA) found that many organizations’ privacy statements fail to meet common privacy principles; less than 1% of organizations had language stating which types of third parties could access user data; only 2% of organizations had explicit language about data retention; only 32% of organizations had “readable” statements based on OTA standards [Internet’s Society’s Online Trust Alliance]

 

AI and the future of work

57% of technology companies do not expect technological advances will displace any of their workers in the next five years; 29% of respondents expect job displacement and 68% plan to retain workers by offering reskilling programs; software development (63%), data analytics (54%), engineering (52%), and AI/machine learning (48%) are the tech skills in highest demand [Consumer Technology Association survey of 252 tech business leaders]

Business adoption of AI

17% of 30 Global 500 companies have reported the use of AI/machine learning at scale and 30% reported selective use in specific business functions; in 3 years, 50% expect to be using AI/machine learning at scale; 26% have deployed RPA at scale across the enterprise or major functions; 65% say their use of RPA today is selective and siloed by individual groups or functions; in 3 years, 83% expect to have RPA deployed at scale; companies investing in AI report achieving on average 15% productivity improvements for the projects they are undertaking; most companies reported that their investments in AI-related talent and supporting infrastructure will increase approximately 50% to 100% in the next three years [KPMG 2019 Enterprise AI Adoption Study based on in-depth interviews with senior leaders at 30 of the world’s largest companies and other sources]

85% of organizations surveyed have a data strategy and 77% have implemented some AI-related technologies in the workplace, with 31% already seeing major business value from their AI efforts; top business functions for gaining most value from AI are sales (35%) and marketing (32%) and top technologies are machine learning (34%), chatbots (34%), and robotics (28%) [Mindtree survey of 650 IT leaders in the US and UK]

Expected business impact of AI

Top AI priorities for the next 3 to 5 years: customer and market insights that will refine personalization, driving sales and retention; back office and shared services automation to remove repetitive human tasks; finance and accounting streamlined to improve efficiency and compliance; analysis of unstructured voice and text data for specific functional use cases [KPMG 2019 Enterprise AI Adoption Study based in in-depth interviews with senior leaders at 30 of the world’s largest companies and other sources]

85% of institutional investors view AI as an investment risk that could potentially provoke societal backlash as well as geopolitical tension; 52% of the investors surveyed, who stated AI was a risk, also regarded it an opportunity, whereas 33% saw it as only a risk and 7% regard it as an opportunity only [BNY Mellon Investment Management and CREATE-Research in-depth, structured interviews with 45 CIOs, investment strategists and portfolio managers among pension plans, asset managers and pension consultants in 16 countries and a literature survey of about 400 widely respected research studies]

AI research successes

A deep learning algorithm, trained on non-imaging and sequential medical records, predicted the development of non-melanoma skin cancer in an Asian population with 89% accuracy [JAMA Dermatology]

Researchers at MIT developed a machine learning model that can estimate a patient’s risk of cardiovascular death. Using just the first fifteen minutes of a patient’s raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories. Patients in the top quartile were nearly seven times more likely to die of cardiovascular death when compared to the low-risk group in the bottom quartile. By comparison, patients identified as high risk by the most common existing risk metrics were only three times more likely to suffer an adverse event compared to their low-risk counterparts [MIT CSAIL]

Source: https://www.forbes.com/sites/gilpress/2019/09/18/artificial-intelligence-ai-stats-news-ai-is-actively-watching-you-in-75-countries/#a6335dc58092