Artificial intelligence is everywhere. On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines.
A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything. That’s not exactly the case. A.I., machine learning, deep learning, and robotics are all fascinating and separate topics. They all serve as an integral piece of the greater future of our tech. Many of these categories tend to overlap and complement one another.
The broader AI field of study is an extensive place where you have a lot to study and choose from. Understanding the difference between these four areas are foundational to getting a grasp and seeing the whole picture of the field.
Artificial intelligence
At the root of AI technology is the ability for machines to be able to perform tasks characteristic of human intelligence. These types of things include planning, pattern recognizing, understanding natural language, learning and solving problems.
There are two main types of AI: general and narrow. Our current technological capabilities fall under the latter. Narrow AI exhibits a sliver of some kind of intelligence – be it reminiscent of an animal or a human. This machine’s expertise is as the name would suggest, is narrow in scope. Usually, this type of AI will only be able to do one thing extremely well, like recognize images or search through databases at lightning speed.
General intelligence would be able to perform everything equally or better than humans can. This is the goal of many AI researchers, but it is a ways down the road.
Current AI technology is responsible for a lot of amazing things. These algorithms help Amazon give you personalized recommendations and makes sure your Google searches are relevant to what you’re looking for. Mostly any technologically literate person uses this type of tech every day.
One of the main differentiators between AI and conventional programming is the fact that non-AI programs are carried out by a set of defined instructions. AI on the other hand learns without being explicitly programmed.
Here is when the confusion starts to take place. Often times – but not all the time – AI utilizes machine learning, which is a subset of the AI field. If we go a little deeper, we get deep learning, which is a way to implement machine learning from scratch.
Furthermore, when we think about robotics we tend to think that robots and AI are interchangeable terms. AI algorithms are usually only one part of a larger technological matrix of hardware, electronics and non-AI code inside of a robot.
Robot... or artificially intelligent robot?
Robotics is a branch of technology that concerns itself strictly with robots. A robot is a programmable machine that carries out a set of tasks autonomously in some way. They’re not computers nor are they strictly artificially intelligent.
Many experts cannot agree on what exactly constitutes a robot. But for our purposes, we’ll consider that it has a physical presence, is programmable and has some level of autonomy. Here are a few different examples of some robots we have today:
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Roomba (Vacuum Cleaning Robot)
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Automobile Assembly Line Arm
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Surgery Robots
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Atlas (Humanoid Robot)
Some of these robots, for example, the assembly line robot or surgery bot are explicitly programmed to do a job. They do not learn. Therefore we could not consider them artificially intelligent.
These are robots that are controlled by inbuilt AI programs. This is a recent development, as most industrial robots were only programmed to carry out repetitive tasks without thinking. Self-learning bots with machine learning logic inside of them would be considered AI. They need this in order to perform increasingly more complex tasks.
What’s the difference between Artificial Intelligence and Machine Learning?
At its foundation, machine learning is a subset and way of achieving true AI. It was a term coined by Arthur Samuel in 1959, where he stated: “The ability to learn without being explicitly programmed.”
The idea is to get the algorithm to learn or be trained to do something without being specifically hardcoded with a set of particular directions. It is the machine learning that paves way for artificial intelligence.
Arthur Samuel wanted to create a computer program that could enable his computer to beat him in checkers. Rather than create a detailed and long-winding program that could do it, he thought of a different idea. The algorithm that he created gave his computer the ability to learn as it played thousands of games against itself. This has been the crux of the idea ever since. By the early 1960s, this program was able to beat champions in the game.
Over the years, machine learning developed into a number of different methods. Those being:
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Supervised
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Semi-supervised
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Unsupervised
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Reinforcement
In a supervised setting, a computer program would be given labeled data and then be asked to assign a sorting parameter to them. This could be pictures of different animals and then it would guess and learn accordingly while it trained. Semi-supervised would only label a few of the images. After that, the computer program would have to use its algorithm to figure out the unlabeled images by using its past data.
Unsupervised machine learning doesn’t involve any preliminary labeled data. It would be thrown into the database and have to sort for itself different classes of animals. It could do this based on grouping similar objects together due to how they look and then creating rules on the similarities it finds along the way.
Reinforcement learning is a little bit different than all of these subsets of machine learning. A great example would be the game of Chess. It knows a set amount of rules and bases its progress on the end result of either winning or losing.
Deep learning
For an even deeper subset of machine learning comes deep learning. It’s tasked with far greater types of problems than just rudimentary sorting. It works in the realm of vasts amounts of data and comes to its conclusion with absolutely no previous knowledge.
If it was to differentiate between two different animals, it would distinguish them in a different way compared to regular machine learning. First, all pictures of the animals would be scanned, pixel by pixel. Once that was completed, it would then parse through the different edges and shapes, ranking them in a differential order to determine the difference.
Deep learning tends to require much more hardware power. These machines that run this are usually housed away in large data centers. Programs that use deep learning are essentially starting from scratch.
Of all the AI disciplines, deep learning is the most promising for one day creating a generalized artificial intelligence. Some current applications that deep learning has spurned have been the many chatbots we see today. Alexa, Siri and Microsoft’s Cortana can thank their brains because of this nifty tech.
A new cohesive approach
There have been many seismic shifts in the tech world this past century. From the computing age to the internet and to the world of mobile devices. These different categories of tech will pave the way for a new future. Or as Google CEO Sundar Pichai put it quite nicely:
“Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an A.I. first world.”
Artificial intelligence in all of its many forms combined together will take us on our next technological leap forward.
More than half of 18 to 25 year-olds in the workforce are considering quitting their job. And they're not the only ones.
In a report called The Next Great Disruption Is Hybrid Work – Are We Ready?, Microsoft found that as well as 54% of Generation Z workers, 41% of the entire global workforce could be considering handing in their resignation.
Similarly, a UK and Ireland survey found that 38% of employees were planning to leave their jobs in the next six months to a year, while a US survey reported that 42% of employees would quit if their company didn't offer remote working options long term.
New work trends
Based on surveys with over 30,000 workers in 31 countries, the Microsoft report – which is the latest in the company's annual Work Trend Index series – pulled in data from applications including Teams, Outlook and Office 365, to gauge productivity and activity levels. It highlighted seven major trends, which show the world of work has been profoundly reshaped by the pandemic:
- Flexible work is here to stay
- Leaders are out of touch with employees and need a wake-up call
- High productivity is masking an exhausted workforce
- Gen Z is at risk and will need to be re-energized
- Shrinking networks are endangering innovation
- Authenticity will spur productivity and wellbeing
- Talent is everywhere in a hybrid world
"Over the past year, no area has undergone more rapid transformation than the way we work," Microsoft CEO Satya Nadella says in the report. "Employee expectations are changing, and we will need to define productivity much more broadly – inclusive of collaboration, learning and wellbeing to drive career advancement for every worker, including frontline and knowledge workers, as well as for new graduates and those who are in the workforce today. All this needs to be done with flexibility in, when, where and how people work."
Organizations have become more siloed
While the report highlights the opportunities created by increased flexible and remote working patterns, it warns that some people are experiencing digital exhaustion and that remote working could foster siloed thinking. With the shift to remote working, much of the spontaneous sharing of ideas that can take place within a workplace was lost. In its place are scheduled calls, regular catch-ups and virtual hangouts. The loss of in-person interaction means individual team members are more likely to only interact with their closest coworkers.
"At the onset of the pandemic, our analysis shows interactions with our close networks at work increased while interactions with our distant network diminished," the report says. "This suggests that as we shifted into lockdown, we clung to our immediate teams for support and let our broader network fall to the wayside. Simply put, companies became more siloed than they were pre-pandemic."
Burnout or drop out
One of the other consequences of the shift to remote and the reliance on tech-based communications has been the phenomenon of digital burnout. And for those who have most recently joined the workforce, this has been a significant challenge.
The excitement of joining a new employer, maybe even securing a job for the first time, usually comes with meeting lots of new people, becoming familiar with a new environment and adapting to new situations. But for many, the pandemic turned that into a daily routine of working from home while isolated from co-workers.
"Our findings have shown that for Gen Z and people just starting in their careers, this has been a very disruptive time," says LinkedIn Senior Editor-at-Large, George Anders, quoted in the report. "It's very hard to find their footing since they're not experiencing the in-person onboarding, networking and training that they would have expected in a normal year."
But it is perhaps the data around quitting that is one of the starkest indications that change is now the new normal. Being able to work remotely has opened up new possibilities for many workers, the report found. If you no longer need to be physically present in an office, your employer could, theoretically, be located anywhere. Perhaps that's why the research found that "41% of employees are considering leaving their current employer this year".
In addition to that, 46% of the people surveyed for the Microsoft report said they might relocate their home because of the flexibility of remote working.
A hybrid future
In looking for ways to navigate their way through all this change, employers should hold fast to one word, the report says – hybrid. An inflexible, location-centred approach to work is likely to encourage those 41% of people to leave and find somewhere more to their tastes. Those who are thinking of going to live somewhere else, while maintaining their current job, might also find themselves thinking of quitting if their plans are scuppered.
But remote working is not a panacea for all workforce ills. "We can no longer rely solely on offices to collaborate, connect, and build social capital. But physical space will still be important," the report says. "We're social animals and we want to get together, bounce ideas off one another, and experience the energy of in-person events. Moving forward, office space needs to bridge the physical and digital worlds to meet the unique needs of every team – and even specific roles."
Bosses must meet challenges head on
Although the majority of business leaders have indicated they will incorporate elements of the hybrid working model, the report also found many are out of touch with workforce concerns more widely.
For, while many workers say they are struggling (Gen Z – 60%; new starters – 64%), and 54% of the general workforce feels overworked, business leaders are having a much better experience. Some 61% said they were 'thriving', which is in stark contrast to employees who are further down the chain of command.
Jared Spataro, corporate vice president at Microsoft 365, writes in the report: "Those impromptu encounters at the office help keep leaders honest. With remote work, there are fewer chances to ask employees, 'Hey, how are you?' and then pick up on important cues as they respond. But the data is clear: our people are struggling. And we need to find new ways to help them."
Reprinted with permission of the World Economic Forum. Read the original article.
