AI News, Artificial Intelligence Isn't a Priority for Small Businesses, Here's Why artificial intelligence

Elon Musk’s brain-interface company is promising big news. Here’s what it could be.

Neuralink, the secretive company bankrolled by Elon Musk to develop brain-computer interfaces, will provide its first public update later today in an event streamed over the internet.

This could be the big reveal of what the mysterious company has been up to since Musk announced it two years ago, and hired a pack of leading university neuroscientists to pursue his goal of connecting human brains directly to artificial-intelligence software.

A look at the available evidence suggests Neuralink will show off a “high-bandwidth” connection to a monkey brain—one able to extract lots of information by recording the activity of many neurons at once, using ultrathin flexible electrodes.

Previously, experimental brain interfaces have been used to let paralyzed humans move cursors and robotic arms with their thoughts, to try to listen in to their speech, to stimulate memory formation, and to try to treat depression.

Based on speculation from outside experts, former insiders, and the past work of scientists Neuralink has hired, the company may be using what’s called a neural “sewing machine” to inject flexible wire electrodes into a monkey’s brain and then record from a very large number of neurons at once.

Members of the company’s founding team have worked on brain interfaces as widely different as tiny metal seeds (so-called “neural dust”) powered by sound waves and holograms that convey data into animal brains.

“If you want to augment a human, you need to do a lot of basic work first.” (We emailed several Neuralink employees for comment, including Hodak, but didn’t hear back.) Some scientists are concerned about focusing too much on the sheer number of electrodes that can be stuffed into a brain.

In 2017 DARPA handed out $65 million to build a “brain modem” that could connect with a million neurons, but José-Alain Sahel, who is working on brain implants to restore vision at the University of Pittsburgh, told me he’s suggested that the agency deemphasize the numerical goal.

“What’s important for treatments is whether the signal is meaningful.” One factor behind the drive for a dense web of connections is the hope that if the brain can be measured at a larger scale, then the buzzing of thousands, or millions, of neurons could be fed into a deep-learning program—like those in development by OpenAI, another Musk venture.

Building the AI-Powered Organization

We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in core practices that support widespread adoption.

Firms struggle to move from the pilots to companywide programs—and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.

While cutting-edge technology and talent are certainly needed, it’s equally important to align a company’s culture, structure, and ways of working to support broad AI adoption.

Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.

Diverse teams can also think through the operational changes new applications may require—they’re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows.

The new system rapidly analyzed the vast range of scheduling permutations, using first one algorithm to distill hundreds of millions of options into millions of scenarios, and then another algorithm to boil down those millions into just hundreds, ranking the optimal schedules for each participant.

(Our research shows that the majority of workers will need to adapt to using AI rather than be replaced by AI.) When a large retail conglomerate wanted to get its employees behind its AI strategy, management presented it as an existential imperative.

In sharing their vision, the company’s leaders put a spotlight on workers who had piloted a new AI tool that helped them optimize stores’ product assortments and increase revenue.

For example, if a company has relationship managers who pride themselves on being attuned to customer needs, they may reject the notion that a machine could have better ideas about what customers want and ignore an AI tool’s tailored product recommendations.

The bank created a booklet for relationship managers that showed how combining their expertise and skills with AI’s tailored product recommendations could improve customers’ experiences and increase revenue and profit.

In one of our surveys nearly 90% of the companies that had engaged in successful scaling practices had spent more than half of their analytics budgets on activities that drove adoption, such as workflow redesign, communication, and training.

Automated processes that don’t need human intervention, such as AI-assisted fraud detection, can deliver a return in months, while projects that require human involvement, such as AI-supported customer service, are likely to pay off over a longer period.

An Asian Pacific retailer determined that an AI initiative to optimize floor space and inventory placement wouldn’t yield its complete value unless the company refurbished all its stores, reallocating the space for each category of goods.

The tool provided only a small fraction of the total return anticipated, but the managers could get the new items into stores immediately, demonstrating the project’s benefits and building enthusiasm for the multiyear journey ahead.

Often leaders simply ask, “What organizational model works best?” and then, after hearing what succeeded at other companies, do one of three things: consolidate the majority of AI and analytics capabilities within a central “hub”;

One consolidated its AI and analytics teams in a central hub, with all analytics staff reporting to the chief data and analytics officer and being deployed to business units as needed.

Our research shows that companies that have implemented AI on a large scale are three times as likely as their peers to have a hub and 2.5 times as likely to have a clear methodology for creating models, interpreting insights, and deploying new AI capabilities.

We’ve seen many organizations squander significant time and money—spending hundreds of millions of dollars—up front on companywide data-cleaning and data-integration projects, only to abort those efforts midway, realizing little or no benefits.

In contrast, when a European bank found that conflicting data-management strategies were hindering its development of new AI tools, it took a slower approach, making a plan to unify its data architecture and management over the next four years as it built various business cases for its AI transformation.

To encourage customers to embrace the AI-enabled services offered with its smart, connected equipment, one manufacturer’s sales and service organization created a “SWAT team” that supported customers using the product and developed a pricing plan to boost adoption.

By concentrating its data scientists, engineers, and many other gray-area experts within the hub, the company ensured that all business units and functions could rapidly access essential know-how when needed.

For example, an organization might have high business complexity and need very rapid innovation (suggesting it should shift more responsibilities to the hub) but also have very mature AI capabilities (suggesting it should move them to the spokes).

Each generally includes the manager in charge of the new AI tool’s success (the “product owner”), translators, data architects, engineers and scientists, designers, visualization specialists, and business analysts.

For example, at the Asian Pacific retailer that was using AI to optimize store space and inventory placement, an interdisciplinary execution team helped break down walls between merchandisers (who determined how items would be displayed in stores) and buyers (who chose the range of products).

By inviting both groups to collaborate on the further development of the AI tool, the team created a more effective model that provided a range of weighted options to the buyers, who could then choose the best ones with input from the merchandisers.

To this end some are launching internal AI academies, which typically incorporate classroom work (online or in person), workshops, on-the-job training, and even site visits to experienced industry peers.

Here the focus is on constantly sharpening the hard and soft skills of data scientists, engineers, architects, and other employees who are responsible for data analytics, data governance, and building the AI solutions.

Strategic decision makers, such as marketers and finance staff, may require higher-level training sessions that incorporate real business scenarios in which new tools improve decisions about, say, product launches.

They regularly meet with staff to discuss the data, asking questions such as “How often are we right?” and “What data do we have to support today’s decision?” The CEO of one specialty retailer we know is a good example.

One airline company, for instance, used a shared scorecard to measure rate of adoption, speed to full capability, and business outcomes for an AI solution that optimized pricing and booking.

The CEO of the specialty retailer starts meetings by shining a spotlight on an employee (such as a product manager, a data scientist, or a frontline worker) who has helped make the company’s AI program a success.

For instance, he promoted the category manager who helped test the optimization solution during its pilot to lead its rollout across stores—visibly demonstrating the career impact that embracing AI could have.

Since their sales incentives were also closely tied to contracts and couldn’t easily be changed, the organization ultimately updated the AI model to recognize the trade-off between profits and the incentives, which helped drive user adoption and lifted the bottom line.

As they work more closely with colleagues in other functions and geographies, employees begin to think bigger—they move from trying to solve discrete problems to completely reimagining business and operating models.

Companies that excel at implementing AI throughout the organization will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own.

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The discipline known as Artificial Intelligence (AI) dates from 1956 when, at a workshop held on the Dartmouth College campus in the USA, the MIT cognitive scientist, Marvin Minsky, said, “Within a generation...the problem of creating ‘artificial intelligence’ will substantially be solved.” While AI has taken longer to develop than Minsky predicted, it’s being facilitated by the imminent spread of 5G and its associated effects.

Some see its development as a boon – perhaps in terms of analysing large amounts of data to help medical science develop cures for cancer.

Key findings Defining AI as: ‘the theory and development of computer systems that are able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making and translation between languages’, the survey’s key findings are: While 43% of respondents already use AI or feel prepared to use it within the next 12 months, almost 66% of respondents think that their employer isn’t prepared to adopt AI within the next year.

Looking at characteristic results from professionals in each of the geographies surveyed, UK professionals are generally unwelcoming of AI - although 55% of those surveyed agreed they know little about AI.

Spanish professionals rank highest in the belief that AI will make their work more efficient and of better quality but only 17% think that these improvements will translate into improved job opportunities.

key message for HR professionals about organisations adopting and using AI is that not only do employees need to feel safe and understand more about AI so that they can maintain higher motivation and morale, they also need to feel confident in engaging with this new technology.

In particular, leaders – and HR/ L&D professionals - need to address a key theme emerging from this report: employees feel a need for enhanced internal communication to manage potential negative perceptions.

Facial and emotional recognition; how one man is advancing artificial intelligence

Despite what you hear about artificial intelligence, machines still can't think like a human, but in the last few years they have become capable of learning.

And suddenly, our devices have opened their eyes and ears and cars have taken the wheel.

Today, artificial intelligence is not as good as you hope and not as bad as you fear, but humanity is accelerating into a future that few can predict.

His 50 million social media followers want to be seen in the same frame because of his talent for engineering and genius for wealth.

Scott Pelley: I wonder, do you think people around the world have any idea what's coming in artificial intelligence?

It's a kind of artificial intelligence that has been made possible by three innovations: super fast computer chips, all the world's data now available online, and a revolution in programming called 'deep learning.'

Well, you simply show the computer ten million pictures of men in various kinds of dress.

So, Face++ tagged me as male, short hair, black long sleeves, black long pants.

When engineers discover that error, they'll show the computer a million gray suits and it won't make that mistake again.

The machine notices concentration or distraction to pick out for the teacher those students who are struggling or gifted.

It can also create a student profile and know where the student got stuck so the teacher can personalize the areas in which the student needs help.

This English teacher is connected to a class 1,000 miles away in a village called Duh-Fang.

Many students in Duh-Fang are called 'left behinds' because their parents left them with family when they moved to the cities for work.

Kai-Fu Lee: Well, Silicon Valley has been the single epicenter of the world technology innovation when it comes to computers, internet, mobile and AI.

College student Monica Sun showed us how more than a billion chinese are using their phones to buy everything, find anything and connect with everyone.

With a pliant public, the leader of the Communist Party has made a national priority of achieving AI dominance in ten years.

Scott Pelley: There are those, particularly people in the west who worry about this AI technology as being something that governments will use to control their people and to crush dissent.

Kai-Fu Lee: Basically chauffeurs, truck drivers anyone who does driving for a living their jobs will be disrupted more in the 15 to 20 year time frame and many jobs that seem a little bit complex, chef, waiter, a lot of things will become automated we'll have automated stores, automated restaurants, and all together in 15 years, that's going to displace about 40% of the jobs in the world.

The challenge of AI is this 40%, whether it is 15 or 25 years, is coming faster than the previous revolutions.

This system can read faces and grade papers but it has no idea why these children are in this room or what the goal of education is.

But if you're talking about AGI, artificial general intelligence, I would say not within the next 30 years, and possibly never.

I believe there's a lot of love and compassion that is not explainable in terms of neural networks and computation algorithms.

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