This month the McKinsey Global Institute released a discussion paper called “Notes from the AI Frontier: Insights from Hundreds of Use Cases” that measures the recent progress of artificial intelligence business applications and identifies industries and use cases where AI has the greatest potential to unlock value.

There are a number of different ‘problem types’ identified by the analysts, each of which are suited to particular deep learning or AI techniques. These problem types include classification (based on a set of training data, categorizing new inputs as belong to one of a set of categories), continuous estimation (based on a set of training data, estimate the next numeric value in a sequence), clustering (a system creates a set of categories for which individual data instances have a set of common or similar characteristics), optimization, anomaly detection, ranking, recommendations, and data generation.

The two most important takeaways are that the best AI systems have consistently outperformed humans since 2015, and specifically in the transport and logistics industry, AI can improve performance over other analytics techniques by 89%. In other words, the technology is maturing and becoming commercially viable, and transportation and logistics stands to gain more from AI than almost any other industry.

McKinsey also found that AI will create $400-500B in value for transport and logistics alone, representing 4.9-6.4% of industry revenues. 

“Application of AI techniques such as continuous estimation to logistics can add substantial value across many sectors,” the analysts wrote. “AI can optimize routing of delivery traffic, thereby improving fuel efficiency and reducing delivery times. One European trucking company has reduced fuel costs by 15 percent, for example. By using sensors that monitor both vehicle performance and driver behavior, drivers receive real-time coaching, including when to speed up or slow down, optimizing fuel consumption and reducing maintenance costs. In another example, an airline uses AI to predict congestion and weather-related problems in order to avoid costly cancellations. For an airline with 100,000 flights per day, a 1 percent reduction in cancellations can make a material difference.”

“Applying some of these advanced analytics can actually give the small carriers just as many community-based opportunities as a large carrier. That’s actually going to allow us to grow together,” said Tim Leonard, CTO of TMW Systems, in a recent interview. 

“We’re looking at the business model of full automation,” Leonard told FreightWaves. “I know within trucking that’s kind of far-fetched, but there are specific applications that machine learning components can perform well at. Today large enterprises have a huge set of algorithms, and what we’re looking at is the introduction of algorithms as a service, which sets us up for machine learning,” Leonard added.

DHL and IBM’s new white paper “Artificial Intelligence in Logistics” stated that “In an increasingly complex and competitive business world, companies that operate global supply chains are under unprecedented pressure to deliver higher service levels at flat or even lower costs. At the same time internal functions of global corporations such as accounting, finance, human resources, legal, and information technology are plagued by large amounts of detail-oriented, repetitive tasks. Here, AI presents a significant opportunity to save time, reduce costs and increase productivity and accuracy with cognitive automation.”

Predictive demand and capacity planning that uses artificial intelligence to scour online browsing data, YouTube views, and social media conversations could calculate probabilities of a spike in demand for a specific product and predict the next fidget spinner-like fad. IBM’s Watson has learned how to perform visual inspection of logistics assets like railroad cars, and computer vision tools are being trained to manage inventory.

What’s important for executives at this stage, though, is to precisely define the business problems that can be solved with AI, and ask if the problem actually demands an AI solution. The report warns that “A cautious stocktake of business value drivers such as cost reduction, improved customer experience, and efficiency gains through better insights is required before starting an AI project.”

“As supply chain leaders continue their digital transformation journey, AI will become a bigger and inherent part of day-to-day business, accelerating the path towards a proactive, predictive, automated, and personalized future for logistics. Ultimately, AI will place a premium on human intuition, interaction, and connection allowing people to contribute to more meaningful work,” DHL and IBM concluded.

SOURCE: https://www.freightwaves.com/news/techpertise/artificial-intelligence-primetime

There is an enduring fear in the music industry that artificial intelligence will replace the artists we love, and end creativity as we know it.

As ridiculous as this claim may be, it’s grounded in concrete evidence. Last December, an AI-composed song populated several New Music Friday playlists on Spotify, with full support from Spotify execs. An entire startup ecosystem is emerging around services that give artists automated songwriting recommendations, or enable the average internet user to generate customized instrumental tracks at the click of a button.

But AI’s long-term impact on music creation isn’t so cut and dried. In fact, if we as an industry are already thinking so reductively and pessimistically about AI from the beginning, we’re sealing our own fates as slaves to the algorithm. Instead, if we take the long view on how technological innovation has made it progressively easier for artists to realize their creative visions, we can see AI’s genuine potential as a powerful tool and partner, rather than as a threat.

Ironically, AI-generated music is nothing “new” in music history. As early as the 1950s, experimental composers were writing music using randomized statistical models. A few decades later, David Bowie worked with former Universal Music Group CTO Ty Roberts to build the Verbasizer -- a program that prompted Bowie to input as many as 25 sentences and word groups into a series of windows, which the program then reordered randomly into new, potentially significant lyrical combinations.

“I can then re-imbue [the Verbasizer] with an emotive quality if I want to, or take it as it writes itself,” said Bowie, as memorialized in the documentary Inspirations. “Some of the things I’ll actually empathize with terrifically.” In other words, Bowie saw this chaotic (if elementary) AI as a means of elevating, rather than stifling, his art -- and he ultimately used many of the Verbasizer’s output in his iconic Berlin Trilogy of albums in the late 1970s, which many consider to be his best work.

Hence, what’s new in 2018 isn’t so much the technology: it’s the money. Major labels, streaming services, VC firms and other stakeholders are investing more and more cash in trying to build scalable, AI-generated music products for the masses.

In Sep. 2016, Sony’s Computer Science Laboratories (CSL) built an AI called Flow Machines that collaborated with songwriter Benoît Carré to write a song in the style of The Beatles, titled “Daddy’s Car.” Less than a year later, CSL’s director François Pachet left Sony to join Spotify as head of the streaming service’s Creator Technology Research Lab, which is headquartered in Paris and focuses on “making tools to help artists in their creative process.”

Almost immediately upon joining Spotify, Pachet reunited with Carré to help promote a new, AI-composed music project on the streaming platform called SKYGGE. One of SKYGGE’s hit singles -- “Hello Shadow,” featuring Kiesza -- appeared on Spotify’s flagship New Music Friday playlist in Dec. 2017, as well as on localized NMF playlists in the U.K., Norway and Scandinavia.

Through its in-house Magenta project, Google is also developing deep-learning algorithms for generating songs, drawings and other artworks. One of its most popular music projects, Performance RNN, uses neural networks to give expressive, human-like timing and dynamics to otherwise stagnant, machine-generated MIDI files. All of Magenta’s tools are open-source, and real artists are already using these tools to write their own songs.

In the startup world, companies like Splice and Amadeus Code are building similarly AI-facilitated assistants for songwriters and producers. Three of the 21 startups in the Techstars Music accelerator roster to date -- Amper, Popgun and SecondBrain -- have built their core product around AI-generated music. Both Amper and Popgun have closed additional funding rounds from the likes of Khosla Ventures, Two Sigma Ventures, Horizons Ventures and Foundry Group since graduating from Techstars.

“Looking at our wider Music portfolio, these types of startups are raising a disproportionate share of capital from outside investors,” Bob Moczydlowsky, managing director of Techstars Music, tells Billboard. “I think elite investors and venture firms are particularly aligned with the field and potential of creative AI.”

Yet, despite all this money flowing in, creative AI still faces financial and legal resistance from many people in music, particularly from legacy label execs who built their businesses around the exploitation of human-owned copyright.

Considering that music is inherently subjective and emotional, the notion of having a machine quantify and optimize for “aesthetic quality” understandably ruffles some feathers. In other operational areas, however, major labels are gradually warming up to the notion that data and “gut” don’t simply cancel each other out, but rather are most powerful when they coexist. Warner Music Group’s recent acquisition of Sodatone, a startup using algorithms to streamline and improve the A&R process, is a prime example of such a paradigm transforming artist development.

What makes creative AI particularly contentious is that labels and streaming services -- who are locked in an arguably faulty mutual dependency -- actually have opposite stakes in the outcome of just how good AI-generated music gets. While labels want to continue making money from their copyrights, streaming services want to stop relinquishing nearly 80 percent of their revenue to third-party rights holders.

In particular, Spotify’s motivations for investing more in AI-generated music may be the same reason why Uber, Lyft and other rideshare companies are investing more in self-driving cars -- namely banking on future costs plummeting in order to justify heavy upfront R&D spend. In this vein, Spotify’s chief R&D officer Gustav Söderström characterizing the company's consumer-facing vision as "self-driving music" is no coincidence.

The tension between labels and streaming services around creative automation was catapulted into the limelight in July 2017, when media outlets and industry execs accused Spotify of placing “fake artists” in its mood playlists. It was revealed that Swedish production- and background-music company Epidemic Sound had developed a robust distribution network for mood music on YouTube and successfully translated that network into majority market share on a handful of Spotify playlists like Peaceful Piano, and Deep Focus and Ambient Chill. The coming wave of creative AI will serve as yet another litmus test for how much the music industry can tolerate -- and compete with -- a more functional, utilitarian music streaming ecosystem.

Unfortunately for labels, a handful of the startups working on AI-generated music are also operating on a more utilitarian outlook on the industry. Speaking at the Copyright & Technology Conference in New York in Jan. 2018, Amper Music CEO Drew Silverstein outlined what he understood as a distinction between “artistic music” (e.g. the orchestral soundtrack from Star Wars) and “functional music” (e.g. Muzak-style elevator jingles).

Silverstein declared that Amper's primary product was indeed “functional music, for the contexts that demand it." In his eyes, not only is a functional music product more sellable, but such an approach also widens the market significantly to users who might not necessarily judge a song on its artistic merits alone -- namely film, advertising, gaming and other adjacent industries.

“Videographers and even many large corporations don’t really care about who owns the copyright, as much as they care about what that music can do,” said Silverstein. “Music is very functional for them.”

To mitigate future risk, labels would be wise to invest in and/or acquire AI-generated music startups -- which is already happening indirectly, through partnerships with programs like Techstars Music. But there’s still a legal elephant in the room that may complicate already-frazzled conversations around IP and ownership in music: as of press time, U.S. law doesn’t allow an AI to own a copyright.

Section 102 of the U.S. Code states that “in no case does copyright protection for an original work of authorship extend to any idea, procedure, process, system, method of operation, concept, principle, or discovery.” Musical algorithms fit these criteria almost perfectly: at their heart, they are merely procedures and processes for outputting musical content. The legal complexity lies in figuring out whether human artists using AI tools are truly the “authors” of the end works created, or whether they are simply the programmers behind the AI tools.

Until lawmakers iron out these kinks -- which, if history is any lesson, will take several years if not decades -- the industry will see deals orchestrated across the spectrum on a case-by-case basis. For instance, YouTube personality and singer Taryn Southern formally credited Amper Music as a co-writer in all the tracks for her latest album, I AM AI. On the other end, while many artists have use Google Magenta tools to write their own music, most of them have neither the desire nor the infrastructure to pay royalties back to Google.

As these early deals get under way, another pressing question looms overhead: how do we define “success” for AI-generated music in the first place? Human creativity itself may be the most perplexing “black box” of all -- far more opaque than any algorithm or non-disclosure agreement. If we ourselves struggle to describe how exactly we make music, how can we possibly teach a computer to do it? How can we measure progress and improvement such that we can justify further investment in the space in the long term?

Google is grappling directly with these challenges, having developed many of the most compelling AIs of the past decade, including but not limited to Google Translate and AlphaGo. An important part of why these particular AIs have made such a huge impact on society is that they have beaten humans at solving narrowly-defined problems -- e.g. how to win a board game, or how to translate practically from one language to another.

In contrast, a Magenta researcher, speaking on the condition on anonymity, candidly tells Billboard that “we are working in a domain where we don’t really have any way to evaluate what we’re doing.”

But that openness and flexibility is arguably a strength -- especially for the artist community, which historically felt pressured to conform to what tech companies asked of them, not the other way around. Magenta is involving artists directly in their day-to-day operations, constantly asking artists how they create music and how their current tools could be more user-friendly.

This artist-centric approach may be the ultimate north star for how future creative-AI ventures define success: “It’s not about making the next ‘AI superstar’ album. It’s about empowering creators,” says the Magenta source. “It’s about making sure artists value what we make -- saying that they ‘couldn’t have done it' without Magenta tools -- while ensuring that their resulting creative output still feels like their own.”

Today, there are clear situations where an AI can outperform human artists, but not in the sense of outright displacement. Two of the biggest pain points in any creative process are time and cost, and AI can help reduce both factors significantly. In addition, amidst brutal touring, travel and interview schedules, artists can easily experience exhaustion and be less productive in their songwriting as a result, whereas there is no such thing as “exhaustion” for algorithms; assuming no “bugs,” they can run nonstop at peak performance, 24/7.

Where AI still falls far behind artists -- and where no one in the industry needs to fret about losing their jobs, at least for the time being -- is in building a compelling story around music that will convince fans to pay.

The claim that “AI will replace artists” assumes that we prescribe value only to songs themselves, when in reality musicians are so respected because they are arbiters of culture and context, not just of sound. If they are doing their jobs effectively, the people who help promote and develop artists (labels, distributors, managers, concert promoters, agents, etc.) are also selling the artists’ backstories -- personal histories, cultural and political upbringings, individual passions, etc. -- as much as they are selling the musical works.

In contrast, a typical AI has no “memories” or “experiences” in the world on which to draw for its creativity; just like Google Translate or AlphaGo, it’s working only with a limited set of predetermined rules and rewards. Until we can have an AI tell stories about itself and the music it writes, and until music companies feel comfortable promoting such "AI-generated backstories," human artists will still end up at the top and have the last creative word.

Hence, for all corners of the music industry, today's AI models are no more than evidence-based tools for reducing the time and cost required to make smarter decisions. At least in the short term, creative AI will reshape the industry not by eliminating jobs, but rather by freeing up real estate in the human mind to capitalize on certain perspectives and actions that it could not afford to take previously.

A positive side effect of this shift is that AI-facilitated tools and assistants will continue the work of Garageband, drum machines and other similar technologies in democratizing music creation to more and more aspiring artists all over the world -- which is arguably a net gain for the music industry at large.

“The collective incentive and ability to create has always gone up massively with each new technological advance,” Silverstein said at the Copyright & Technology Conference. “Not only could we do more as composers, but the whole world of musical creation was also opened up to more and more people. If AI is a tool that helps you create when the opportunity isn't otherwise there, then history tells us that you’re going to do it, regardless of the economic incentives.”

In short, AI's aggregate impact on the music industry will not merely be inaction or laziness, but rather increased access and efficiency that enables further intellectual elevation. After all, unlike today’s AI, we ultimately write and dictate the narratives around our own creativity -- for now.

SOURCE: https://www.billboard.com/articles/business/8333911/artificial-intelligence-music-reshaping-destroying-industry

We can all remember the crisply beveled edges of our cheery-yellow No. 2 pencil, the cool, smooth feel of a chalk-powdered blackboard, the gritty red bricks of the schoolhouse walls. Surely that all wasn’t just an illusion?

No, of course not.

But—as it turns out—it kind of is.

The sense of touch (and indeed, all of our senses) is more or less illusory because no sensation stands alone. Stretch out your hands and snap your fingers. This, of course, feels very real. But you’re seeing your fingers, hearing your fingers, and feeling your fingers—and all of these sensations fully correspond.

Now, what if they didn’t?

Virtual Reality (VR) is a great tool for revealing this strange and otherworldly foundation of our everyday sensory perceptions. Sneaky (but, of course, highly ethical) experimentalists such as ourselves can render a completely computer-generated world. If we hand you a pair of controllers that can vibrate on command, we can play tricks.

We can even use this simple apparatus to produce a sensation of touch that “feels like” it originates in the completely empty space between your outstretched hands—an experiment we discuss in our paper in the current issue of Science Robotics.

This VR equivalent of a carnival trick is known as funneling. Each controller provides the user with synchronous vibrotactile stimuli of different amplitudes. In the virtual environment, we show the participant a wooden dowel that they grip with each hand. The dowel is only imaginary, of course—the hands are not physically linked, and the dowel does not exist. We then show a small white marble that seems to knock on the dowel at different locations as we vary the vibratory sensations.

This is where it gets weird. The apparent spatial location of the touch sensation will persist, even if the marble is invisible to the eyes of the beholder. Your brain fills in these gaps and inconsistencies—the vibratory sensations felt by your hands, the (at times imaginary) little white marble knocking on the dowel in the physically empty space in-between. A particular spatial location that happens to be completely outside of your own body.

This is a neat perceptual illusion, but why does it matter?

Well, in virtual environments (or robotic tele-operation tasks), producing higher-fidelity haptic (that is, tactile) sensations of this sort come with the (oft-unstated) assumption that such “improvements” will always yield more a realistic and immersive experience. Using this funneling illusion when the marble was imaginary we can demonstrate an “uncanny valley” effect for haptic sensations, in which the distorted sensations of touch produced in our experiments that were almost right.

As a result, they were exactly wrong, and the immersion was broken.

But this uncanny valley can be shifted or eliminated by subtly manipulating the experimental conditions. For example, if the dowel appears to be inside a smoky-looking cylinder, thus partially obscuring the location of the knocking (but invisible) marble, this provides a plausible reason for any discrepancy between the perceived location of the haptic sensation, versus what we see with our eyes—and the simulation feels more realistic.

Likewise, the sensation can be manipulated based on whether we are simply holding the virtual dowel—thus experiencing the knock of the marble as a passive participant—or actively moving the dowel up and down, thus becoming the ‘agent’ of the sensation through our own actions.

Although demonstrated in our curious VR testbed at present, these effects are rooted in human perception. As such, understanding them more deeply helps us build better and more convincing virtual environments.

SOURCE: https://blogs.scientificamerican.com/observations/if-virtual-reality-feels-almost-right-its-exactly-wrong/

Artificial intelligence’s progress is staggering. Efforts to advance AI concepts over the past 20 years have resulted in some truly amazing innovations. Big data, medical research, and autonomous vehicles are just some of the incredible applications emerging from AI development.

To understand some of the deeper concepts, such as data mining, natural language processing, and driving software, you need to know the three basic AI concepts: machine learning, deep learning, and neural networks. While AI and machine learning may seem like interchangeable terms, AI is usually considered the broader term, with machine learning and the other two AI concepts a subset of it.

It’s likely that you’ve interacted with some form of AI in your day-to-day activities. If you use Gmail, for example, you may enjoy the automatic e-mail filtering feature. If you own a smartphone, you probably fill out a calendar with the help of Siri, Cortana, or Bixby. If you own a newer vehicle, perhaps you’ve benefited from a driver-assist feature while driving.

As helpful as these software products are, they lack the ability to learn independently. They cannot think outside their code. Machine learning is a branch of AI that aims to give machines the ability to learn a task without pre-existing code.

In the simplest terms, machines are given a large amount of trial examples for a certain task. As they go through these trials, machines learn and adapt their strategy to achieve those goals.

For example, an image-recognition machine may be given millions of pictures to analyze. After going through endless permutations, the machine acquires the ability to recognize patterns, shapes, faces, and more.

A well-known example of this AI concept is Quick, Draw!, a Google-hosted game that lets humans draw simple pictures in under 20 seconds, with the machine-learning algorithm trying to guess the drawing. More than 15 million people have contributed more than 50 million drawings to the app.

How do we get machines to learn more than just a specific task? What if we want it to be able to take what it has learned from analyzing photographs and use that knowledge to analyze different data sets? This requires computer scientists to formulate general-purpose learning algorithms that help machines learn more than just one task.

One famous example of deep learning in action is Google’s AlphaGo project written in Lua, C++, and Python code. The AlphaGo AI was able to beat professional Go players, a feat that was thought impossible given the game’s incredible complexity and reliance on focused practice and human intuition to master.

How was a program able to master a game that calls for human intuition? Practice, practice, practice — and a little help from an artificial neural network.

Deep learning is often made possible by artificial neural networks, which imitate neurons, or brain cells. Artificial neural networks were inspired by things we find in our own biology. The neural net models use math and computer science principles to mimic the processes of the human brain, allowing for more general learning.

SOURCE: https://www.roboticsbusinessreview.com/ai/3-basic-ai-concepts-explain-artificial-intelligence/

With the rise of artificial intelligence, several of today’s larger organizations face the issue of restructuring. Several global companies believe their organizational design is less than ideal, but few know how to go about remedying it.

The answer to this is to make sure the global workforce "upskills" to be qualified enough to handle fresh positions. Adapting to a rapidly-changing world of work will be the defining task of our time - organizations ought to invest in their workers to ensure they aren’t left behind. Those with the right skills will be at an advantage to choose how, where and when they work. The rate of advancement and globalisation cannot be offset, but we can invest in employees’ skills to enhance their resilience. Equally important is nurturing learnability and adopting new skills to stay abreast and remain employable.  

As indicated in the trends listed below, while AI-related technological developments may cause sluggish growth in some areas of hiring, it will, as a matter of fact, create jobs too. Below are some of the HR trends that we will see gaining prominence in 2018, some of which will see artificial intelligence playing a supporting role:

1. HR Becomes a Strategic Business Partner

As strategy is usually undertaken by top management, this requires HR to work more closely with top management in order to become a strategic business partner, if not one already. A vast portion of today’s CHRO’s have already begun to implement this. HR ought to focus on understanding the wishes, needs and competencies of employees, which are integral to designing a highly-coveted employee experience and becoming a fantastic employer brand.

2. Renewed Emphasis on Employee Productivity

When filling positions is a time-sensitive priority, it gets tougher for recruitment to be scrupulous. A lack of quality talent causes an increase in the levels of coordination and management required, meaning that productivity goes down.

When the hiring emphasis is on quality and productivity, benefits increase for the organisation as well as for its employees. People analytics can support HR in gathering data to determine the habits and characteristics of the highest performing people and teams. The findings of which can be used in future recruitment and talent development.

3. Focus on People Analytics

People analytics was once a technical discipline owned exclusively by data experts. Today, it has carved out its own niche as a business and managerial discipline; meaning 2018 will see an increase in the number of organizations with a stand-alone people analytics function. Among other tasks, the PA team will be responsible for creating means by which to link team leaders and senior management. The real challenge will then be to generate insights that can inform decisions relating to people, teams and the best ways to support them.

4. Learning in Real-time

With micro-learning gaining popularity, learning modules are being increasingly broken up into more digestible pieces, providing employees with access to learning material when they need it, AKA ‘just in time’ learning. An L&D approach can also be adopted through gamification, with new virtual and augmented reality offerings making it more enjoyable. The challenge will be to track and measure the performance of an organization’s human capital, to be able to find or create the most appropriate learning solution.

These trends point to a superior workplace experience in 2018. And although companies may face new hurdles in making them a reality, they will likely be met with a more streamlined approach, higher productivity and greater engagement.

SOURCE: https://www.entrepreneur.com/article/312152

The concepts of big data and machine learning are not necessarily new to businesses world-wide; however, what is rather extraordinary is the developing movement in industry to combine the two concepts propelling Artificial Intelligence applications to new heights of learning and adaptability.

Big Data in Business

Big Data is essentially the realization that all of the information being collected online could be harnessed by businesses. The collection of Big Data was revolutionary because companies became capable of making smarter decisions simply by having more access to data and consequently insights than ever before.

Machine Learning in Business

Intelligent Automation has changed how many businesses operate as well. Robotic Process Automation is able to take in requests and complete them efficiently. Beyond the standard automation, however, is the ability for a machine to learn beyond its programming. This allows the software to adjust and evolve as new information is brought in, making it more efficient than ever. Through Machine Learning, businesses find that lower-end work is completed faster and processed more accurately than ever before.

Big Data + Machine Learning = Faster & Smarter Decisions

Alone, both Big Data and Machine Learning have their parts in the future of all business around the world, but together, they become so much more. As a business takes in a variety of collected data, it can be challenging to sort the data into trends that are logical and helpful. Through the use of Intelligent Automation, however, this information can be processed. Through Machine Learning, the data can not only be identified and sorted, but as more information is collected by the software, the machine is capable of learning more from it. This kind of Intelligent Automation learns from the data, resulting in data trends that might have been too complex for most human workers.

Businesses that utilize both Big Data and Machine Learning are capable of making faster and smarter business decisions than ever before. The data that comes in and is analyzed by an RPA can provide insights that would have been mere guesses in previous years.

Knowledge is power, and that statement could not be any truer for those companies trying to succeed in business today. Machine Learning is that power which is required to harness the knowledge gained from Big Data, enabling businesses to make faster and smarter decisions with an efficiency that was not even imaginable a few years ago.

Artificial intelligence is already being used to address big challenges like climate change, world economic crises, global hunger, and aging populations, but how can AI benefit us as individuals in our roles as citizens, parents, caregivers, and advocates? How can it support those who are most vulnerable?

Today’s pioneers in AI technology recognize that tackling the world’s big challenges and people’s personal obstacles requires both machine intelligence and human brainpower. It is not either-or. It’s about augmented intelligence, not artificial, and it offers great hope and opportunity. To proceed ethically, AI must be handled in a principled and responsible manner.

The application, called #Here4U Military Version, is enabled by the IBM Watson cognitive platform, and is being done in partnership with Queen’s University and the Canadian Institute for Military and Veteran Health Research and IBM.

Additionally, IBM is working with HR services company Morneau Shepell on a solution to help people in distress. Ava is a newly launched chatbot IBM Canada designed to guide people to mental wellness resources and services when they have questions about their own mental wellness.

In most workplaces, the main impediment people experience isn’t lack of services, but how to gain knowledge of and access to available help. Whether someone is suffering from PTSD or in a stressful work situation, this AI app helps people overcome their self and societal stigmas as they search for available help channels. The goal is to make sure that many options are available to enable more people to get help sooner.

As AI technology grows and our ability to harness the available data expands, we will see more examples like #Here4U that offer tremendous advantages for society and our individual lives. As with every new advancement in technology, when ethical principles are applied, all benefit.

To that end, IBM has developed three principles – purpose, transparency, and skills – that guide how we use AI, and these offer a guide for others to consider.

The purpose of AI and cognitive systems must be to augment human intelligence. Technology, products, services, and policies should enhance and extend human capability, expertise, and potential. These systems will be embedded in the products, services, and processes which make society function and improve people’s lives, but they will always remain within human control.

Students, workers, and citizens need opportunities to acquire the skills and knowledge needed to interact safely, securely, and effectively with AI systems. The societal and economic benefits of this cognitive era depend upon the engagement and support of people who will perform the new types of jobs that emerge.

As we discover new ways that AI will address global and individual challenges, it is crucial that society wrestle with guiding principles for the ethical use of this technology. Business leaders have a responsibility to lead that discussion.

SOURCE: https://betakit.com/how-to-build-ai-responsibly-in-canada/