Companies that Learn to Scale Digital Innovation See Strong Returns

Technology, RODI — Image Source: Industry Week

Industrial companies have found highly effective ways to scale their digital innovation efforts, resulting in much higher returns on digital investment, according to new Industry X.0 research from Accenture.

These “Champions” consistently scale more of their proofs of concepts (PoCs) and achieve higher-than-average returns on their efforts compared to their peers.

For the research, which was unveiled at Hannover Messe in Germany on April 3, Accenture surveyed 1,350 senior and C-suite executives from industrial businesses across 13 industries, representing both discrete and process manufacturing. The key finding: while all companies surveyed were investing to scale their innovation efforts beyond the PoC stage, only a small group of them – the 22% Champions – reached expected earnings.

These “Champions” consistently scale more of their proofs of concepts (PoCs) and achieve higher-than-average returns on their efforts compared to their peers.

“Scaling innovation is critical for digital transformation success, but clearly presents a challenge for many organizations,” said Mike Sutcliff, group chief executive of Accenture Digital. “The key question is, therefore – how can companies succeed at it? The Champions we found in our research are very strategic. They leverage four specific management best practices to specify the value they’re seeking to create, and then focus on changing their organization. To them, it’s not about scaling more – even though they do that – it’s about scaling better.”

The Rewards for Being a Champion

These “Champions” consistently scale more of their proofs of concepts (PoCs) and achieve higher-than-average returns on their efforts compared to their peers.

The Rewards for Being a Champion

The best-performing companies in the sample scale more than 50% of their PoCs. They also expect much higher returns from their efforts than their peers. Most importantly, they tend to not only meet these high expectations – but to exceed them.

Accenture’s research found that,…Continue reading

Article source: https://www.industryweek.com/technology-and-iiot/companies-learn-scale-digital-innovation-see-strong-returns

Artificial Intelligence Is Set To Transform The Doctor’s Toolkit in 2019

Artificial Intelligence, Medical, Doctors — Image Source: Analytics India Magazine

By MARTIN F.R.

Looks like 2019 will be a different ball game altogether as AI will make deeper inroads into shaping the modern physician’s toolkit. According to a report by Tractica, the healthcare AI market is projected to reach $34 billion globally by 2025. In this article, we list down how AI will overhaul the healthcare landscape.

6 Areas Where AI Will Impact The Most In 2019

1 | AI will play a crucial role in diagnosing

It will be used to help physicians identify the severity of what is wrong with the patient and provide insights into why they are experiencing symptoms which will help doctors decide on the most effective treatment for the patient.
Coronary heart disease and cancer are two good examples where the influence of AI is seen heavily.

2 | AI-enabled models will also be used in conjunction with powerful computer algorithms

This will solve complex equations of blood flow and assess the impact of blockages, aiding physicians in determining, vessel by vessel, if enough blood is reaching the heart.
Also, the level of detail provided will help ensure that physicians understand the extent of disease and develop the best pathway for care.

3 | AI-trained detection algorithms will help physician’s mark-up tumour boundaries on imaging scans for cancer patients.

AI tools will map out the organ on each image and create a 3D model, which can then be assessed by a physician to accurately detect tumours.
This practice will help physicians become more efficient and speed up the reliability of cancer diagnoses and enable more targeted treatment decisions.

4 | To monitor chronic health conditions

Once the patients leave the direct care, AI-based monitoring tools and apps come into play which is built into gadgets or smartwatches to monitor their health, thereby providing the ability for consumers to securely share health information with their physicians.
The new Apple Watch which has the capability to record electrocardiograms (ECGs) and use AI to detect common heart arrhythmias is a good example of how AI is making inroads.
Newer applications will be made to collect medical data from consumers which will turn as insights to be used by physicians
This data will help physicians keep up-to-date on their patients’ health and fill in the gaps between appointments.

5 | AI will also enhance the physician’s workflow by increasing their efficiency and confidence in their patient care decisions.

6 | AI technology companies will also develop tools that can be approved by appropriate regulatory pathways by understanding holistically the medical problems.

Outlook

AI has the potential to transform the way physicians deliver high-quality, cost-effective, diagnostic and treatment services. However, what’s required is more education and resources directed towards AI that focuses primarily at all three levels be it the patient, provider or the payer. Moreover, necessary infrastructure must be in place to maximise the day-to-day impact on AI-based patient care decisions that need to be made.

As more and more physicians are open to investigating and adopting technologies that are utilising artificial intelligence in their own practice and the hospitals they work for, the partnership between AI and humans in the healthcare industry will become more mainstream in 2019.

Article source: https://www.analyticsindiamag.com/ai-transform-doctors-toolkit-2019/

The ‘artificial intelligence’ in your new smart gadget may not be what you think

Artificial Intelligence, Technology — This briefcase is listening to a pair of Jabra Elite 85h headphones. Jabra

By Rob Verger

Walk the halls of the Consumer Electronics Show, or browse gadgets online, and you might hear that a gizmo has, or uses, AI. Artificial intelligence is a broad, catch-all term, and so it can be hard to know what it actually means for a product to have AI. Does it mean you can talk to it, and it talks back? Can make decisions on its own? Is going to lead a robot army to harvest the organs of everyone you know?

The powerful technology is also becoming ubiquitous enough that’s it’s common to see it employed, and touted by, small companies you haven’t heard of, as opposed to just the big players, like Amazon or Google. Plus, companies that make gadgets that connect to a voice assistant, like Alexa, may use that as reason enough to call their product “smart.”

Here’s how to make sense of it all.

A key point to understand is that artificial intelligence isn’t just synonymous with a voice assistant. Those voices, like Alexa, make use of AI, to be sure—but there’s much more going on in the world of artificial intelligence.

Under the umbrella of AI is the large, dynamic field of machine learning. Frequently, when you encounter artificial intelligence in a product, it’s because it’s employing machine learning under the hood to do something, make a decision, or both. At its simplest, machine learning involves engineers feeding data into software, which then learns from it. The resulting algorithms can accomplish different tasks.

Listen up

Here’s an example: Danish company Jabra announced their latest headphones at CES, the Elite 85h. They advertise the new $299 ‘phones as using “AI technology,” and they do, in the form of machine learning. They’re not “artificially intelligent” in the sense that they can read your mind and start talking to you, but the way they make use of AI is indeed smart.

Perhaps predictably, they call the feature in question SmartSound. “It listens to the environment the user is in,” says Fred Lilliehook, a senior product marketing manager at Jabra. “It automatically adapts the audio experience.”

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Artificial intelligence – boons and banes

Artificial Intelligence, Deep Learning — Image Source: CIO Review

By CIOReview

Artificial intelligence (AI) is the source of both enthusiasm and skepticism. With humans and machines joining in the array, AI promises for immense transformational possibilities. From the need to forecast electricity demands or autonomous cars to customer engagement in industries, the need for AI-adoption is everywhere. However, with the boons also some challenges arise after the adoption.

Challenges:

• Organizations provide initial outlays for software and costs for cloud implementation, costs for training employees and continue training of the AI system when business processes change.

• There various types of technologies are available and it is a rigorous job to narrow down which technology matches specifically which job.

• AI applications rely on huge volumes for decision-making. Machine learning uses sensitive and personal data to learn and enhance itself. This increases the vulnerability of data and the possibility of a data breach.

• Inability to predict ROI (return on investment) precisely.

Opportunities:

Understanding big data: Big data, after data collection, is analyzed to understand current trends, patterns, and make critical prediction. Big data helps in decision taking tasks, supported by vast amounts of data and enables businesses in predicting, mitigating risks, and personalizing.

Robots: Robots are probing solar system for science of life, building car motors and motor plants or making milkshake in the kitchen or defusing bombs. There are robot astronauts, soldiers who are replaced by drones and robot soldiers.

Understanding emotion: AI has the potential more than only processing requests and synthesizing data. Companies are now developing technologies that can understand sentiments. The idea is that by understanding emotions, AI could predict persons’ needs as humans do.

AI offers the ability to transform a business by improving its operational efficiencies, generating higher profit margins, and providing insights about the business. The journey of AI implementation can be complicated and risky but at the end, it can be highly profitable.

Article Source: https://www.cioreview.com/news/artificial-intelligence-boons-and-banes-nid-27622-cid-175.html

Ford brings artificial intelligence to Edge AWD

Artificial Intelligence, Cars, Automobiles, Technology — Image credit: Motor Matters photo

By Lyndon Conrad Bell, MOTOR MATTERS

The 2019 Ford Edge sees definite nods toward greater efficiencies — and in a Ford-first, Artificial Intelligence is introduced in its all-wheel drive system.

Ford reduces engine choices from three to two, with the elimination of the normally aspirated 3.5-liter V-6. The base powerplant for the 2019 Ford Edge is now a 250-horsepower turbocharged inline four, making 275 lb.-ft. of torque. Minor tweaks have increased engine output by 5 horsepower, though the torque figure remains the same.

The other 2019 engine offering is a 335-horsepower, 2.7-liter turbocharged V-6 with 380 lb.-ft. of torque. Updates extracted an additional 20 horsepower and 30 lb.-ft. of torque. Applied to the new 2019 Edge ST version, it marks the first time a true high-performance variant of the midsize crossover has been presented for public consumption. Adjustments to the driveline further improve acceleration off the line, making the Edge ST a genuinely quick vehicle. Ford quotes 0-to-60-mph acceleration of just under 6 seconds.

That it accomplishes this while also achieving better fuel economy than its less-powerful Edge Sport predecessor is truly remarkable. The EPA said Edge ST is good for 19 mpg in the city and 26 on the highway; an increase of 2 mpg in both regards. While both engines trade six-speed automatic transmissions for eight-speed automatics, each gets its own specific gearing. All-wheel drive is standard with the ST and optional for all other Edge versions.

The automaker reports that in a first-for-Ford technology its new artificial intelligence calculates quicker than the human brain. Based on information received from dozens of high-tech sensors, the 2019 Edge can determine in a fraction of a second whether all-wheel drive is needed. The system is called “all-wheel-drive disconnect.”

According to Ford, the system detects…Continue Reading…

Article source: https://www.chron.com/cars/article/Ford-brings-artificial-intelligence-to-Edge-AWD-13432113.php

Three Movies That Altered Our Perception Of Artificial Intelligence

Artificial Intelligence, Tech — Image source: TV Overmind

By Nat Berman

When Lost in Space first aired in 1965, America was introduced to the idea of a friendly robot/human connection. In a now-famous episode, we see the robot concerned for a human when the robot warns young Will Robinson of an impending threat by uttering, “Danger, Will Robinson!” The robot was not given a name, but displayed human characteristics associated with artificial intelligence like laughter, sadness, and the ability to mock the other characters.

In today’s movies, some robots are friendly with humans, but many robots are portrayed as evil and kill both heroes and villains. Although we know television isn’t real, we can’t deny how the stories told on screen influence our perceptions.

The Lost in Space robot demonstrates mild AI capabilities compared to the films that came after it. Here are 3 movies that have shaped the way we perceive artificial intelligence:

1. The Terminator series plus Reboot

The Terminator is one of many in a long list of titles featuring killer robots. In The first Terminator, Arnold Schwarzenegger plays a cyborg sent back in time to kill the mother of a man responsible for leading a rebellion that enslaved humanity. In subsequent episodes, his abilities as a cyborg get more powerful and lethal.

The biggest question raised by Terminator is whether artificial intelligence can be implanted into a human being to make them superhuman. Dr. Simon Watson, a robotics expert, says the technology needed to create Terminator-style killer robots is decades away, but there is currently a real threat of smart weapons.

Watson says, “Robots that think for themselves is the big fear – a SkyNet type deal – but the real problem is the automation of weapon platforms. Experts in the field agree the coming smart weapons won’t be machines that will rise up to kill people, but will be unmanned aircraft and tanks that can recognize threats on the battlefield and make the decision to kill (or not).

We’re left to wonder if these machines will be able to distinguish friend from foe, and exercise proportionate violent force. U.S. drone strikes in Afghanistan killed at least 85 civilians in 2016, so the concern is valid.

Disruptive Tech with Innovation Management

Technology, Innovation, — Image credit: Pixabay

BY EVAN MORRIS | techspective

Economists have been studying human behavior for centuries, determining how our ever-changing desires fuel supply-and-demand paradigms. They have conducted in-depth analysis of the legal frameworks responsible for current trading regimens for SMEs, and government enterprises. These centuries-old systems, developed though they may be are now being upended by new-age technology that is adding value, cutting costs, and increasing efficiency like never before. Innovative new technologies are being designed to be more inclusive, more transparent, and more rewarding. These systems allow for easy, quick, and efficient information dissemination.

Of course, these concepts encompass so many other attendant technologies. Innovation management technology has the capacity to radically transform financial, legal, medical and information dissemination networks across the board. These disruptive processes are already yielding results in the way that enterprises function. Global management systems have evolved dramatically over the years. Prior to his passing, Nobel laureate Douglas North pioneered new institutional economics. The institutional economics that he referred to were the rules and frameworks that were established, and the systems that were used to enforce trade. We have evolved from hunter gatherers to formal institutions where banks were involved, and later to online institutions. The global economy has now metamorphosed into a large interconnected online system. Within this online network lies innovation management par excellence.

Dramatic Trade Transformations Pay Dividends

Human economic activity is now facilitated by way of online institutions through ecommerce operations including Alibaba, Amazon, eBay and the like. Every new iteration that humankind develops through innovation and dynamism – the latest being online connectivity via the Internet – is designed to lower levels of uncertainty when creating value. Before, management systems relied on the banking sector and government for their operational functionality.

Now, we can use sophisticated technology without banking or government to define our new-age economic system. This has resulted in dramatic innovation management systems and a new breed of employees who are bringing this dynamism to life. For many folks, the concept of innovation management remains shrouded in mystery. The power of these new-age management systems is extraordinary, and enterprises are using disruptive innovation management solutions to facilitate cutting-edge systems that are rendering traditional economic models defunct.

Unleashing Corporate Innovation and Coupling It with State-of-the-Art Technology

We are seeing a merger of innovative technology and innovation management right before our eyes. The traditional system of authoritarian management has given way to collective systems where feedback is encouraged, fostered, and engendered in organizations since the employees have a vested interest in furthering their own objectives alongside those of the enterprise. Even new ICO companies are springing up rapidly, fusing blockchain tech with innovation management systems. These enterprises are proposing new-age solutions that have traditional institutions running scared. The magic of innovation management is that it offers exponential benefits to corporations since these companies are drawing on the synergistic power of their human capital.

It is against this backdrop that companies are able to reshape their activities, redesign their products and services, and refocus their creative wells of inspiration to draw from their strongest assets – their employees. Whether it’s incremental innovation or disruptive innovation, it is thanks to out-of-the-box thinking in concert with innovation management technology that we are able to achieve amazing feats of human ingenuity, trustless systems, and efficient operations.

Article source: https://techspective.net/2018/11/17/disruptive-tech-with-innovation-management/

Five Powerful Ways for Leaders to Unlock Innovation

Innovation, Technology — Image source: Bigstock

Innovation is about disrupting the comfortable for the sake of improvement. No one innovates to decrease a process or reduce the benefit. People innovate to bring life to a higher plane because improvement is a deeply held instinct.

Yet innovation doesn’t just happen – it requires cultivation of forces that will disrupt the comfortable. If you are the leader, your number one job is to provide an environment of innovation that will bring your team or business to the next level. Below are five powerful ways for leaders to unlock innovation within their organization.

#1 – Look For and Enlist Your Innovators

Every team has their innovators. They are the ones who speak out by making suggestions on how to do things better, often challenging the status quo. Innovators have big ideas and want to share them with the teams, which can create friction.

As a leader, your job is to enlist these innovators and turn their energy into actions that cultivate results. For example, a manager of one of our professional services groups presented me with an idea to build a new training program. The idea has merit; however, it needs more vetting beyond the “I have an idea” conversation. I asked him to build out the framework of the plan, and develop a model of how it could work within our business. He left the conversation energized and ready to take on the next phase of this innovation.

As a leader, you must have an open door policy of idea vetting. The key is willingness to listen and encourage innovators so they will be motivated to share their creative thinking.

#2 – Create Safety

Leaders create safe environments. Obviously, physical safety is the highest priority, but emotional safety is also important. Emotional safety gives people the opportunity to challenge and question the status quo without fear of retribution.

Strong leaders know that emotional safety is a key leadership tenet and it is not an accident. I recall a situation when two colleagues worked on an idea that resulted in strong results for the team. But during a company meeting, an executive took credit for their work, naming them only as a creative force behind the win. The team that had worked so hard were hurt. To fix the problem, the executive had to go back and quickly correct the misinformation.

Emotional safety is critical to a successful innovation engine. When team members see that action is taken to protect them and give them their due credit, they feel safe to try again.

#3 – Disrupt the Comfortable

Innovation’s job is to challenge the status quo and disrupt the comfortable, which brings with it complaints and dissatisfaction for those being disrupted. As a leader, your job is to get out in front of disruption through strong communications. No one likes surprises and unless you are clear with your messaging, the teams will revolt and hunker down for a fight.

Look for your allies in this process; you will need support to help move innovation forward. Enlist new recruits who see what the innovators see. Your goal is to get people to recognize that although disruption is painful, it is a necessary part of the innovation you need for a more successful organization.

#4 – Sponsor Initiatives

Leaders bring forth initiatives and sponsor them through funding and emotional support. This means you must have some control or influence over budgets, and you’ll need to allocate funds to promote your initiatives.

However, funding is only half of the equation. You will also need to support your team as they run into obstacles. Innovation is about calling out current processes as weak, flawed, or broken. Of course, the people within the current process may…Continue reading…

Article source: https://www.cio.com/article/3320019/leadership-management/five-powerful-ways-for-leaders-to-unlock-innovation.html

Artificial intelligence — Who is responsible for the outcomes?

BY KAREN GRAHAM

Artificial intelligence (AI) is playing an ever-increasing role in our modern world, but as the technology progresses and becomes ever-more complex and autonomous, it also becomes harder to understand how it works.

Actually, most people have very little knowledge of how artificial intelligence works, or for that matter, how broadly it is used in everything from daily financial transactions to determining your credit score.

Take the stock market, for example. Only a tiny amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It’s preprogrammed so that if the price of soybeans or oil goes down, all kinds of additional steps will take place.

And that is the whole point of using artificial intelligence algorithms — everything goes so fast, thousands of seconds faster than the human mind can calculate. The problem with this is that AI still has a long way to go before it becomes the pervasive force that has been promised, and this begs the question: should we put our trust in it?

Artificial Intelligence: Are We Effectively Assessing Its Business Value?

By: Richard Boire, Senior Vice President, Environics Analytics

As most data science practitioners know, artificial intelligence (AI) is not new and has been explored by academia back as far back as the fifties. The real core of AI is the branch of mathematics related to neural nets which have been explored both by academia as well as data science practitioners. A number of practitioners including myself familiarized ourselves with these techniques which became one more item within the data scientist toolkit. For those of us involved in using predictive analytics to predict consumer behaviour related to marketing and risk, logistic regression and decision trees in many cases performed at about the same level as neural nets. In some cases such as fraud where there were typically a much larger volume of records, neural nets did exceed the more traditional type of modelling techniques.

But the appetite for AI deployment was always negated by its lack of its explainability to the business stakeholder and as mentioned above the minimal examples of its superior performance relative to the more traditional techniques.

So what changed and what has led to all the excitement about AI. In order to better understand this evolution, one needs to focus on the research. Research in this area for decades always focussed on how these tools could better classify images. Back in the nineties, I remember reading numerous articles from publications where the ability to classify images was approximately 40%-50%. In the last 5 years, though, this accuracy has now achieved levels of 95%+. This game breaking change was caused by two factors with the first factor being related to technology and how data could be processed.

Data and Big Data could now be processed and consumed using parallel processing as opposed to sequential processing. Meanwhile, this newfound technical capability allowed practitioners to consume exponentially much larger volumes of data for analytics (both advanced and non-advanced) purposes. The consumption of these extremely large volumes then allowed users to explore the notion of more complex type neural nets or deep learning, which is the ability to utilize many hidden layers and many nodes as opposed to a single hidden layer with few nodes that was the common occurrence within a restricted sequential data processing environment. This ability to more fully leverage the power of artificial intelligence was the second factor which now improved the image classification accuracy to 95%+.

With this breakthrough, AI had to be more seriously explored as another option in improving results. But does that mean that we should blindly adopt AI in all our business processes. Certainly, we are seeing the emergence of applications to better detect fraud through improved image recognition while enhanced customer service is the outcome of improved AI-developed chat boxes. Many more applications are being explored and which are expected to provide further disruption to an already changing economy. But let’s discuss the notion of AI within the world of predicting consumer behaviour both from a marketing as well as a risk behaviour.

The use of data science and machine learning to predict consumer behaviour has been an ongoing business discipline for many decades. Success for seasoned data science practitioners in this area was never..Continue Reading

Article Source: https://www.predictiveanalyticsworld.com/patimes/artificial-intelligence-are-we-effectively-assessing-its-business-value/9736/