Artificial Intelligence Needs Oversight

EVERY TIME YOU post a photo, respond on social media, make a website, or possibly even send an email, your data is scraped, stored, and used to train generative AI technology that can create text, audio, video, and images with just a few words. 

This has real consequences: OpenAI researchers studying the labor market impact of their language models estimated that approximately 80 percent of the US workforce could have at least 10 percent of their work tasks affected by the introduction of large language models (LLMs) like ChatGPT, while around 19 percent of workers may see at least half of their tasks impacted. 

We’re seeing an immediate labor market shift with image generation, too. In other words, the data you created may be putting you out of a job.

When a company builds its technology on a public resource—the internet—it’s sensible to say that that technology should be available and open to all. But critics have noted that GPT-4 lacked any clear information or specifications that would enable anyone outside the organization to replicate, test, or verify any aspect of the model. 

Some of these companies have received vast sums of funding from other major corporations to create commercial products. For some in the AI community, this is a dangerous sign that these companies are going to seek profits above public benefit.Code transparency alone is unlikely to ensure that these generative AI models serve the public good. 

There is little conceivable immediate benefit to a journalist, policy analyst, or accountant (all “high exposure” professions according to the OpenAI study) if the data underpinning an LLM is available. We increasingly have laws, like the Digital Services Act, that would require some of these companies to open their code and data for expert auditor review. 

And open source code can sometimes enable malicious actors, allowing hackers to subvert safety precautions that companies are building in. Transparency is a laudable objective, but that alone won’t ensure that generative AI is used to better society.     READ MORE...

AI Needs to be REGULATED

For most of the past decade, public concerns about digital technology have focused on the potential abuse of personal data. People were uncomfortable with the way companies could track their movements online, often gathering credit card numbers, addresses, and other critical information. They found it creepy to be followed around the web by ads that had clearly been triggered by their idle searches, and they worried about identity theft and fraud.

Those concerns led to the passage of measures in the United States and Europe guaranteeing internet users some level of control over their personal data and images—most notably, the European Union’s 2018 General Data Protection Regulation (GDPR). 

Of course, those measures didn’t end the debate around companies’ use of personal data. Some argue that curbing it will hamper the economic performance of Europe and the United States relative to less restrictive countries, notably China, whose digital giants have thrived with the help of ready, lightly regulated access to personal information of all sorts. (Recently, however, the Chinese government has started to limit the digital firms’ freedom—as demonstrated by the large fines imposed on Alibaba.) 

Others point out that there’s plenty of evidence that tighter regulation has put smaller European companies at a considerable disadvantage to deeper-pocketed U.S. rivals such as Google and Amazon.

But the debate is entering a new phase. As companies increasingly embed artificial intelligence in their products, services, processes, and decision-making, attention is shifting to how data is used by the software—particularly by complex, evolving algorithms that might diagnose a cancer, drive a car, or approve a loan. 

The EU, which is again leading the way (in its 2020 white paper “On Artificial Intelligence—A European Approach to Excellence and Trust” and its 2021 proposal for an AI legal framework), considers regulation to be essential to the development of AI tools that consumers can trust.  READ MORE...

AI and ChatGPT Threaten Humanity

As tech experts warn that the rapid evolution of artificial intelligence could threaten humanity, OpenAI's ChatGPT weighed in with its own predictions on how humanity could be wiped off the face of the Earth.

Fox News Digital asked the chatbot to weigh in on the apocalypse, and it shared four possible scenarios how humanity could ultimately be wiped out.

"It's important to note that predicting the end of the world is a difficult and highly speculative task, and any predictions in this regard should be viewed with skepticism," the bot responded. "However, there are several trends and potential developments that could significantly impact the trajectory of humanity and potentially contribute to its downfall."

Fears that AI could spell the end of humanity has for years been fodder for fiction but has become a legitimate talking point among experts as tech rapidly evolves – with British theoretical physicist Stephen Hawking issuing a dire warning back in 2014  

"The development of full artificial intelligence could spell the end of the human race," he said then. Hawking died in 2018.

The sentiment has only intensified among some experts nearly a decade later, with tech giant Elon Musk saying this year that the tech "has the potential of civilizational destruction."   READ MORE...

AI Replaces US Jobs

The growing strength of artificial intelligence threatens millions of jobs, but if regulators stay away, the emerging tech may make society wealthier and more productive.

History has repeatedly shown the same result for other technological advances dating back to the Industrial Revolution, economist Peter St. Onge said.

"Throughout history, we've gone through tremendous technological revolutions. Generally, technologies kill jobs," St. Onge, with the Heritage Foundation, told Fox News Digital. "What happened? Well, you know, we had lots of new jobs. Almost nobody today works on a farm.

"This is sort of the way of the world," he added. The reason why you see technological improvements for any labor-involving function is in order to kill jobs - which is also known as saving work.

St. Onge pointed to the early 1800s, when most people worked on farms, and how the dawn of the mechanization of agriculture killed such employment as farmers turned to machines instead of hiring teams of laborers.  READ MORE...

Artificial Intelligence

Sam Altman of OpenAI

It was a blockbuster 2022 for artificial intelligence. The technology made waves from Google’s DeepMind predicting the structure of almost every known protein in the human body to successful launches of OpenAI’s generative A.I. assistant tools DALL-E and ChatGPT. 

The sector now looks to be on a fast track toward revolutionizing our economy and everyday lives, but many experts remain concerned that changes are happening too fast, with potentially disastrous implications for the world.

Many experts in A.I. and computer science say the technology is likely a watershed moment for human society. But 36% don’t mean that as a positive, warning that decisions made by A.I. could lead to “nuclear-level catastrophe,” according to researchers surveyed in an annual report on the technology by Stanford University’s Institute for Human-Centered A.I., published earlier this month.

Almost three quarters of researchers in natural language processing—the branch of computer science concerned with developing A.I.—say the technology might soon spark “revolutionary societal change,” according to the report. 

And while an overwhelming majority of researchers say the future net impact of A.I. and natural language processing will be positive, concerns remain that the technology could soon develop potentially dangerous capabilities, while A.I.’s traditional gatekeepers are no longer as powerful as they once were.

“As the technical barrier to entry for creating and deploying generative A.I. systems has lowered dramatically, the ethical issues around A.I. have become more apparent to the general public. Startups and large companies find themselves in a race to deploy and release generative models, and the technology is no longer controlled by a small group of actors,” the report said.  READ MORE...

Pausing Chat GPT

AN open letter signed by hundreds of prominent artificial intelligence experts, tech entrepreneurs, and scientists calls for a pause on the development and testing of AI technologies more powerful than OpenAI’s language model GPT-4 so that the risks it may pose can be properly studied.

It warns that language models like GPT-4 can already compete with humans at a growing range of tasks and could be used to automate jobs and spread misinformation. The letter also raises the distant prospect of AI systems that could replace humans and remake civilization.

“We call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4 (including the currently-being-trained GPT-5),” states the letter, whose signatories include Yoshua Bengio, a professor at the University of Montreal considered a pioneer of modern AI, historian Yuval Noah Harari, Skype cofounder Jaan Tallinn, and Twitter CEO Elon Musk.

The letter, which was written by the Future of Life Institute, an organization focused on technological risks to humanity, adds that the pause should be “public and verifiable,” and should involve all those working on advanced AI models like GPT-4. It does not suggest how a halt on development could be verified, but adds that “if such a pause cannot be enacted quickly, governments should step in and institute a moratorium,” something that seems unlikely to happen within six months.

Microsoft and Google did not respond to requests for comment on the letter. The signatories seemingly include people from numerous tech companies that are building advanced language models, including Microsoft and Google. Hannah Wong, a spokesperson for OpenAI, says the company spent more than six months working on the safety and alignment of GPT-4 after training the model. She adds that OpenAI is not currently training GPT-5.  READ MORE...

Take ChatGPT to the Next Level

CHATGPT AND TOOLS like it have made AI available to the masses. We can now get all sorts of responses back on almost any topic imaginable. These bots can come up with sonnets, code, philosophy, and more.

However, while you can just type anything you like into ChatGPT and get it to understand you, there are ways of getting more interesting and useful results out of the bot. This “prompt engineering” is becoming a specialized skill of its own.

Sometimes all it takes is the addition of a few more words or an extra line of instruction and you can get ChatGPT responses that are a level above what everyone else is seeing—and we've included several examples below.

For the purposes of this guide, we tested these prompts with GPT-4: The latest version of ChatGPT at the time of writing, but only available to some users. However, they should work fine with older versions of ChatGPT too.

Get Your Answers in Tabular Form
ChatGPT can give you responses in the form of a table if you ask. This is particularly helpful for getting information or creative ideas. For example, you could tabulate meal ideas and ingredients, or game ideas and equipment, or the days of the week and how they're said in a few different languages.

Using follow-up prompts and natural language, you can have ChatGPT make changes to the tables its drawn and even produce them in a standard format that can be understood by another program (such as Microsoft Excel).

Output Text in the Style of Your Favorite Author
With some careful prompting, you can get ChatGPT out of its rather dull, matter-of-fact, default tone and into something much more interesting—such as the style of your favorite author, perhaps.

You could go for the searing simplicity of an Ernest Hemingway or Raymond Carver story, for instance, or the lyrical rhythm of a Shakespearean play, or the density of a Dickens novel. The end results don't come close to the genius of the actual authors themselves, but it's another way of being more creative with the output you get.  READ MORE...

Nervous About AI

OpenAI entered the Silicon Valley stratosphere last year with the release of two AI products, the image-generator DALLE-2 and the chatbot ChatGPT. (The company recently unveiled GPT-4, which can ace most standardized tests, among other improvements on its predecessor.) Sam Altman (above), OpenAI’s co-founder, has become a public face of the AI revolution, alternately evangelical and circumspect about the potent force he has helped unleash on the world.

In the latest episode of On With Kara Swisher, Swisher speaks with Altman about the many possibilities and pitfalls of his nascent field, focusing on some of the key questions around it. Among them: How do we best to regulate a technology even its founders don’t fully understand? And who gets the enormous sums of money at stake? Altman has lofty ideas for how generative AI could transform society. But as Swisher observes, he sounds like the starry-eyed tech founders she encountered a quarter-century ago — only some of whom stayed true to their ideals.

Kara Swisher: You started Loopt. That’s where I met you.

Sam Altman: Yeah.

Swisher: Explain what it was. I don’t even remember, Sam. I’m sorry.

Altman: That’s no problem. Well, it didn’t work out. There’s no reason to remember. It was a location-based social app for mobile phones.

Swisher: Right. What happened?

Altman: The market wasn’t there, I’d say, is the No. 1 thing.

Swisher: Yeah. Because?

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Hummoid Robots

Humanoid robots are advanced robots that are designed to look and move like humans. They are often equipped with sensors and cameras that allow them to recognize human faces and emotions, respond to voice commands and carry out conversations. Humanoid robots can be programmed to perform a wide range of tasks, such as assisting humans in daily activities, working in manufacturing plants, providing healthcare services, and performing search and rescue operations in hazardous environments.

Compared to conventional robots, humanoid robots provide a number of benefits. One of their main advantages is that they can communicate with people in a more intuitive and natural way. They are, therefore, ideal for fields like education, healthcare and customer service, where human interaction is crucial.

Humanoid robots have a lot of potential, but they are still in the early phases of development and have many obstacles to overcome. One of the biggest issues is their high price, which prevents many businesses and individuals from using them. Furthermore, the creation of humanoid robots demands highly developed engineering abilities as well as expertise in a variety of disciplines, including robotics, artificial intelligence and materials science.

Here are advanced humanoid robots in the world to know.

Atlas
Developed by Boston Dynamics, Atlas is a 1.8-meter-tall humanoid robot designed to perform tasks in rough terrain. It is capable of walking on uneven surfaces and can lift heavy weights. Its advanced capabilities enable it to navigate through difficult terrains and debris to locate and rescue people.

The Atlas robot is appropriate for use in industrial settings since it can move large objects and carry out jobs that are hazardous for people. It is capable of performing precise and accurate assembly lines, welding and painting jobs.

Asimo
Developed by Honda, Asimo is a humanoid robot that is designed to perform tasks like running, walking and climbing stairs. It has advanced sensors that allow it to navigate through complex environments.

Asimo has been used extensively in the fields of research, education and engineering to study human physiology, robotics engineering and human-robot interaction. Researchers now have a better understanding of how to create robots that interact with people in a more intuitive and natural way.  READ MORE...

Robots Taking Over Jobs by 2025

There are two sides to this coin: Robots and AI will take some jobs away from humans — but they will also create new ones. Since 2000, robots and automation systems have slowly phased out many manufacturing jobs — 1.7 million of them. On the flip side, it’s predicted that AI will create 97 million new jobs by 2025.

WILL ARTIFICIAL INTELLIGENCE (AI) REPLACE JOBS?
AI is and will continue to replace some jobs. Workers in industries ranging from healthcare to agriculture and industrial sectors can all expect to see disruptions in hiring due to AI. But demand for workers, especially in robotics and software engineering, are expected to rise thanks to AI.

Some people don’t see it both ways. For example, Sean Chou, former CEO of AI startup Catalytic, thinks robots are stupid —and he’s not alone in his frank assessment.

“All you have to do is type in ‘YouTube robot fail,’” Chou said.

Don’t misunderstand, though; it isn’t that the machines aren’t rising. It’s that they’re rising much more slowly than some of the more breathless media coverage might have you believe — which is great news for most of those who think robots and other AI-powered technology will soon steal their jobs. “Most of” being the operative words.

Types of Jobs AI Will Impact
The consensus among many experts is that a number of professions will be totally automated in the next five to 10 years. A group of senior-level tech executives who comprise the Forbes Technology Council named 15: insurance underwriting, warehouse and manufacturing jobs, customer service, research and data entry, long haul trucking and a somewhat disconcertingly broad category titled “Any Tasks That Can Be Learned.”

HOW MANY JOBS WILL AI REPLACE?
According to the World Economic Forum's "The Future of Jobs Report 2020," AI is expected to replace 85 million jobs worldwide by 2025. Though that sounds scary, the report goes on to say that it will also create 97 million new jobs in that same timeframe.

Kai-Fu Lee, AI expert and CEO of Sinovation Ventures, wrote in a 2018 essay that 50 percent of all jobs will be automated by AI within 15 years.

“Accountants, factory workers, truckers, paralegals, and radiologists — just to name a few — will be confronted by a disruption akin to that faced by farmers during the Industrial Revolution,” Lee wrote.

When considering those developments and predictions, and based on multiple studies — by the McKinsey Global Institute, Oxford University and the U.S. Bureau of Labor Statistics, among others — there is massive and unavoidable change afoot. Research suggests that both specially trained workers and blue-collar workers will be impacted by the continued implementation of AI.

Developments in generative AI tools like ChatGPT and Bard have raised questions about if AI will replace jobs that involve writing. While it’s unlikely that AI will ever match the authentic creativity of humans, it is already being used as a catalyst for writing ideas and assisting with repetitive content creation.  READ MORE...

Machine Learning Matters

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. 

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. 

Here are a few widely publicized examples of machine learning applications you may be familiar with:

The heavily hyped, self-driving Google car? 

The essence of machine learning.
Online recommendation offers such as those from Amazon and Netflix? 

Machine learning applications for everyday life.
Knowing what customers are saying about you on Twitter? 

Machine learning combined with linguistic rule creation.
Fraud detection? 

One of the more obvious, important uses in our world today.

Machine Learning and Artificial Intelligence
While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.

Why is machine learning important?
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.  READ MORE...

Drummers and AI

Clyde Stubblelfield

There’s a moment five minutes into ‘Funky Drummer’ (1970), an instrumental jam by James Brown, when the clouds part and Clyde Stubblefield is left alone. We can hear on the recording Brown instructing his band to ‘give the drummer some’. He tells Stubblefield not to solo, but to ‘just keep what you got’. Even if you’ve never heard the original, you will have heard Stubblefield’s drum break. The looped sample has been used on more than a thousand other tunes. His right hand is playing semiquavers on the hi-hats throughout, with his left foot opening the cymbals to produce an occasional offbeat whisper. His right foot on the bass drum and left hand on the snare are in a conversation. The backbeats on the second and fourth beat of each bar are decorated with what drummers call ‘ghost notes’ on the snare drum, more felt than heard.

In principle, it would be perfectly possible to take each semiquaver, transcribe it, pull the notes from the stave, use readily available software to program them into a grid and fully automate the funky drummer. The beat is repetitive. Drumming is all about patterns, and computers are very, very good with patterns. And yet there is something ineffably human about this performance. The dance of his limbs, the bounce of his sticks and the movement of the air inside his drums combine to produce something undeniably musical. I think a drum machine couldn’t get close. But maybe not everyone cares as much as I do about the nuances of percussion.  READ MORE...

Hyperautomation

Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including: artificial intelligence (AI), machine learning, event-driven software architecture, robotic process automation (RPA), business process management (BPM) and intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code/no-code tools, packaged software, and other types of decision, process and task automation tools.

Hyperautomation is the extension of legacy business process automation beyond the confines of individual processes. By marrying AI tools with RPA, hyperautomation enables automation for virtually any repetitive task executed by business users.

It even takes it to the next level and automates the automation - dynamically discovering business processes and creating bots to automate them. Hyperautomation was identified by Gartner as one of the year’s top 10 strategic technology trends.

With a range of tools like Robotic Process Automation (RPA), machine learning (ML), and artificial intelligence (AI), working in harmony to automate complex business processes—including where subject matter experts were once required—hyperautomation is a means for real digital transformation.

How does Hyperautomation work?

According to Gartner, RPA enriched by AI and ML becomes the core enabling technology of hyperautomation. Combining RPA and AI technologies offers the power and flexibility to automate where automation was never possible before: undocumented processes that rely on unstructured data inputs.  READ MORE...

Robots Will Change the USA

In 2030, robots could be exploring alien worlds and performing surgeries from halfway around the globe. Robotics is one of the fastest evolving fields of technology, and it’s shaping the future of travel, work and exploration. 

Peripheral advancements in AI, computing and IoT are helping elevate things even further. Robotics holds some exciting innovations that will play a central role in daily life worldwide.

Transportation
Few technologies in recent history have been as highly anticipated as autonomous vehicles. Ambitious scientists and engineers have pursued self-driving car technology for centuries. 

In fact, in the 1500s, Leonardo Da Vinci designed a cart that could move on its own. DARPA’s autonomous vehicle challenge in the 2000s sparked a wave of modern R&D surrounding self-driving autos.

Self-driving cars get smarter every year, yet they remain rare on the roads in the 2020s. What makes robotic transportation a challenge is AI computing and human randomness. 

Robots work best with structured, predictable data. Human behavior is never certain. Today’s autonomous vehicle researchers are developing AI models that can accurately and consistently respond to obstacles on the road, especially pedestrians.

Throughout the 2020s, this unique robotics niche may finally get “over the hump” and go mainstream. Tesla has blazed a trail for rudimentary autonomous vehicle tech with its autopilot mode. 

This developed public policy to regulate self-driving cars and increased public adoption. Other leading self-driving car companies are making substantial headway, as well. Waymo began operating its autonomous taxi service in Phoenix in 2020. In 2022, the company announced it would be removing test drivers from the vehicles.

In the 2030s, robot-powered vehicles may be on the road in major cities worldwide. These cars will make travel more accessible, reduce emissions and improve policing behaviors.  READ MORE...

Robots in 2030

Robots are already all around us, whether it’s the automated machines that assemble our vehicles or the virtual assistants that use conversational interfaces to help us around the house. Yet as we’ve seen, they’re not currently suitable for all areas of life. But will that change in the future?

Despite fears of an AI takeover, where machines replace humans as the dominant intelligence on the planet, such a scenario seems unlikely. However, business network PwC predicts that up to 30% of jobs could be automated by robots by the mid-2030s.

Other reports suggest that the stock of robots worldwide could reach 20 million by 2030, with automated workers taking up to 51 million jobs in the next 10 years. So, while they may not take over the world, we can expect to see more robots in our daily lives.
How robots will change the world

According to a report from McKinsey, automation and machines will see a shift in the way we work. They predict that across Europe, workers may need different skills to find work. Their model shows that activities that require mainly physical and manual skills will decline by 18% by 2030, while those requiring basic cognitive skills will decline by 28%.

Workers will need technological skills, and there will be an even greater need for those with expertise in STEM. Similarly, many roles will require socioemotional skills, particularly in roles where robots aren’t good substitutes, such as caregiving and teaching.

We may also see robots as a more integral part of our daily routine. In our homes, many simple tasks such as cooking and cleaning may be totally automated. Similarly, with robots that can use computer vision and natural language processing, we may see machines that can interact with the world more, such as self-driving cars and digital assistants.  READ MORE...

Robots Are On The Horizon

The U.S. market for robotics and artificial intelligence career openings is exploding based on early 2022 trends from job postings on Robots.Jobs, the marketplace specifically for robotics and AI companies looking for talent and for jobseekers looking for the latest industry opportunities. 

In the last 90 days, open positions on Robots.Jobs have increased by more than 500 percent. Newly featured job-posters include autonomous drone hardware and sensors company GreenSight and Intrinsic AI, making industrial robotics accessible and usable for businesses.

"Robotics, IoT and AI careers are in high demand across almost all industries, including industrial, healthcare, biotech, logistics, consumer and more," said Ann P. Walsh, CEO & cofounder, Robots.Jobs. 

"In this competitive job market, talent recruitment requires skill, targeting and focus to attract the most qualified employees. For robotics and artificial intelligence, we are just at the beginning of demand for talent."

Geographies for job growth
Boston, Massachusetts maintains its stronghold on the largest volume of robotics and AI job searches with 25 percent of open positions posted on the Robots. Jobs job board. This growth is in part due to the number of biotech companies actively using robotics and artificial intelligence technologies within their organizations. 

Austin, Texas and Denver, Colorado are also seeing fast growth, with many new innovation centers increasing recruiting efforts for engineering talent. The industry is growing in these states due to lifestyle advantages, a lower cost of doing business, and tax incentives to build a younger, more diverse workforce.  READ MORE...

Robots Already Taking US Jobs

GETTY

If we didn’t have enough to worry about—Covid-19, a nation divided, massive job losses and civil unrest—now we have to be concerned that robots will take our jobs.

The World Economic Forum (WEF) concluded in a recent report that “a new generation of smart machines, fueled by rapid advances in artificial intelligence (AI) and robotics, could potentially replace a large proportion of existing human jobs.” Robotics and AI will cause a serious “double-disruption,” as the coronavirus pandemic pushed companies to fast-track the deployment of new technologies to slash costs, enhance productivity and be less reliant on real-life people.

Millions of people have lost their jobs due to the effects of the Covid-19 pandemic and now the machines will take away even more jobs from workers, according to the WEF. The organization cites that automation will supplant about 85 million jobs by 2025. WEF says there’s nothing to worry about since its analysis anticipates the future tech-driven economy will create 97 million new jobs. Currently, approximately 30% of all tasks are done by machines—and people do the rest. However, by the year 2025, it's believed that the balance will dramatically change to a 50-50 combination of humans and machines.

Management consulting giant PriceWaterhouseCoopers reported, “AI, robotics and other forms of smart automation have the potential to bring great economic benefits, contributing up to $15 trillion to global GDP by 2030.” However, it will come with a high human cost. “This extra wealth will also generate the demand for many jobs, but there are also concerns that it could displace many existing jobs.”

In a dire prediction, WEF said, “While some new jobs would be created as in the past, the concern is there may not be enough of these to go round, particularly as the cost of smart machines falls over time and their capabilities increase.”

Concerns of new technologies disrupting the workforce and causing job losses have been around for a long time. On one side, the argument is automation will create better new jobs and erase the need for physical labor. The counterclaim is that people without the appropriate skills will be displaced and not have a home in the new environment.  READ MORE...

The Future of Work

The future of work is uncertain. Some say robots will dominate the workforce, perhaps eliminating human jobs altogether. The guesswork doesn't stop in imagining possible futures of an even more technology-driven economy. Amid such speculation, it’s easy for business owners to feel unsure about how to plan for the next decade.

In this article, we’ll look at the underlying trend expected to dominate the future workplace: The rise of artificial intelligence (AI). Recently, Gartner made six predictions about how businesses will work by 2028 (full content available to Gartner clients). These got us thinking about two critical impacts AI will have on the future workplace, what these mean for small and midsize businesses, and how business owners and HR leaders can start preparing for these trends in advance.

Prediction #1: AI will replace a number of middle management jobs

Ever imagined taking orders from a robot? This could soon be a reality.

Machine bosses will replace human bosses by the end of the decade. Algorithms that boss employees around, also known as robobosses, will be responsible for assigning work based on skill sets. Robobosses will also decide whether employees will get a promotion and what their salary increases will be.

Here are the top reasons why businesses will be interested in implementing robobosses:

• Data-driven decision-making: It’s true that robots can't show emotions or empathy, but there's one area where they can outperform humans: data-driven decision-making. AI can scan large datasets and apply predictive algorithms to provide actionable insights to business owners. For instance, a roboboss can use factors such as efficiency, skill, knowledge, and motivation level to select team members for projects. This practice will ensure that members with the right skill set and work attitude are chosen, which will increase the chances of timely project completion.
• Cost-effectiveness: Robobosses will take over most middle management tasks, eliminating the need for multiple middle management positions. This will not only lower the salary costs associated with middle managers but also make team management more efficient.
• Availability: Unlike human bosses, robobosses will be available 24/7, making it easier for businesses to manage a global workforce operating in different time zones.
• Impact of this prediction – 2020 vs. 2030

Team composition at the beginning of the decade
Today’s teams comprise employees with expertise in particular skill sets brought together by organizational hierarchy. For instance, a marketing team consists of members who have expertise in search engine optimization (SEO), email marketing, social media marketing, and analytics. Each team has a manager who supervises projects, manages conflicts and people-centric issues, assigns tasks to members, and ensures smooth project execution. The team manager is also responsible for monitoring employee performance and scaling the team size (up or down) as per business requirements.

Team composition at the end of the decade
By the end of the decade, a large number of teams will be autonomous with robobosses responsible for functions currently performed by team managers. Robobosses will manage project allocation, deadlines, delivery, and communication. Smart machines will be responsible for ensuring coordination among different teams, such as sales, marketing, and finance. They will also monitor employee performance and assess the need for upscaling or downsizing based on predicted project workloads.  READ MO

Quantum Artificial Intelligence

A novel proof that certain quantum convolutional networks can be guaranteed to be trained clears the way for quantum artificial intelligence to aid in materials discovery and many other applications. Credit: Los Alamos National Laboratory

Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as "barren plateaus" has limited the application of these neural networks for large data sets, new research overcomes that Achilles heel with a rigorous proof that guarantees scalability.


"The way you construct a quantum neural network can lead to a barren plateau—or not," said Marco Cerezo, co-author of the paper titled "Absence of Barren Plateaus in Quantum Convolutional Neural Networks," published today by a Los Alamos National Laboratory team in Physical Review X. Cerezo is a physicist specializing in quantum computing, quantum machine learning, and quantum information at Los Alamos. "We proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters."

As an artificial intelligence (AI) methodology, quantum convolutional neural networks are inspired by the visual cortex. As such, they involve a series of convolutional layers, or filters, interleaved with pooling layers that reduce the dimension of the data while keeping important features of a data set.

These neural networks can be used to solve a range of problems, from image recognition to materials discovery. Overcoming barren plateaus is key to extracting the full potential of quantum computers in AI applications and demonstrating their superiority over classical computers.  TO READ MORE, CLICK HERE...