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What's the difference between A.I., machine learning, and robotics?
There's a lot of confusion as to what AI, machine learning, and robotics do. Sometimes, they can all be used together.
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.
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:
Roomba (Vacuum Cleaning Robot)
Automobile Assembly Line Arm
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:
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.
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.
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
Scientists used CT scanning and 3D-printing technology to re-create the voice of Nesyamun, an ancient Egyptian priest.
- Scientists printed a 3D replica of the vocal tract of Nesyamun, an Egyptian priest whose mummified corpse has been on display in the UK for two centuries.
- With the help of an electronic device, the reproduced voice is able to "speak" a vowel noise.
- The team behind the "Voices of the Past" project suggest reproducing ancient voices could make museum experiences more dynamic.
Howard et al.<p style="margin-left: 20px;">"While this approach has wide implications for heritage management/museum display, its relevance conforms exactly to the ancient Egyptians' fundamental belief that 'to speak the name of the dead is to make them live again'," they wrote in a <a href="https://www.nature.com/articles/s41598-019-56316-y#Fig3" target="_blank">paper</a> published in Nature Scientific Reports. "Given Nesyamun's stated desire to have his voice heard in the afterlife in order to live forever, the fulfilment of his beliefs through the synthesis of his vocal function allows us to make direct contact with ancient Egypt by listening to a sound from a vocal tract that has not been heard for over 3000 years, preserved through mummification and now restored through this new technique."</p>
Connecting modern people with history<p>It's not the first time scientists have "re-created" an ancient human's voice. In 2016, for example, Italian researchers used software to <a href="https://www.smithsonianmag.com/smart-news/hear-recreated-voice-otzi-iceman-180960570/" target="_blank">reconstruct the voice of Ötzi,</a> an iceman who was discovered in 1991 and is thought to have died more than 5,000 years ago. But the "Voices of the Past" project is different, the researchers note, because Nesyamun's mummified corpse is especially well preserved.</p><p style="margin-left: 20px;">"It was particularly suited, given its age and preservation [of its soft tissues], which is unusual," Howard told <em><a href="https://www.livescience.com/amp/ancient-egypt-mummy-voice-reconstructed.html" target="_blank">Live Science</a>.</em></p><p>As to whether Nesyamun's reconstructed voice will ever be able to speak complete sentences, Howard told <em><a href="https://abcnews.go.com/Weird/wireStory/ancient-voice-scientists-recreate-sound-egyptian-mummy-68482015" target="_blank">The Associated Press</a>, </em>that it's "something that is being worked on, so it will be possible one day."</p><p>John Schofield, an archaeologist at the University of York, said that reproducing voices from history can make museum experiences "more multidimensional."</p><p style="margin-left: 20px;">"There is nothing more personal than someone's voice," he told <em>The Associated Press.</em> "So we think that hearing a voice from so long ago will be an unforgettable experience, making heritage places like Karnak, Nesyamun's temple, come alive."</p>
New research suggests you can't fake your emotional state to improve your work life — you have to feel it.
What is deep acting?<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDk2OS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxNTY5MzA0Nn0._s7aP25Es1CInq51pbzGrUj3GtOIRWBHZxCBFnbyXY8/img.jpg?width=1245&coordinates=333%2C-1%2C333%2C-1&height=700" id="ddf09" class="rm-shortcode" data-rm-shortcode-id="9dc42c4d6a8e372ad7b72907b46ecd3f" data-rm-shortcode-name="rebelmouse-image" data-width="1245" data-height="700" />
Arlie Russell Hochschild (pictured) laid out the concept of emotional labor in her 1983 book, "The Managed Heart."
Credit: Wikimedia Commons<p>Deep and surface acting are the principal components of emotional labor, a buzz phrase you have likely seen flitting about the Twittersphere. Today, "<a href="https://www.bbc.co.uk/bbcthree/article/5ea9f140-f722-4214-bb57-8b84f9418a7e" target="_blank">emotional labor</a>" has been adopted by groups as diverse as family counselors, academic feminists, and corporate CEOs, and each has redefined it with a patented spin. But while the phrase has splintered into a smorgasbord of pop-psychological arguments, its initial usage was more specific.</p><p>First coined by sociologist Arlie Russell Hochschild in her 1983 book, "<a href="https://www.ucpress.edu/book/9780520272941/the-managed-heart" target="_blank">The Managed Heart</a>," emotional labor describes the work we do to regulate our emotions on the job. Hochschild's go-to example is the flight attendant, who is tasked with being "nicer than natural" to enhance the customer experience. While at work, flight attendants are expected to smile and be exceedingly helpful even if they are wrestling with personal issues, the passengers are rude, and that one kid just upchucked down the center aisle. Hochschild's counterpart to the flight attendant is the bill collector, who must instead be "nastier than natural."</p><p>Such personas may serve an organization's mission or commercial interests, but if they cause emotional dissonance, they can potentially lead to high emotional costs for the employee—bringing us back to deep and surface acting.</p><p>Deep acting is the process by which people modify their emotions to match their expected role. Deep actors still encounter the negative emotions, but they devise ways to <a href="http://www.selfinjury.bctr.cornell.edu/perch/resources/what-is-emotion-regulationsinfo-brief.pdf" target="_blank">regulate those emotions</a> and return to the desired state. Flight attendants may modify their internal state by talking through harsh emotions (say, with a coworker), focusing on life's benefits (next stop Paris!), physically expressing their desired emotion (smiling and deep breaths), or recontextualizing an inauspicious situation (not the kid's fault he got sick).</p><p>Conversely, surface acting occurs when employees display ersatz emotions to match those expected by their role. These actors are the waiters who smile despite being crushed by the stress of a dinner rush. They are the CEOs who wear a confident swagger despite feelings of inauthenticity. And they are the bouncers who must maintain a steely edge despite humming show tunes in their heart of hearts.</p><p>As we'll see in the research, surface acting can degrade our mental well-being. This deterioration can be especially true of people who must contend with negative emotions or situations inside while displaying an elated mood outside. Hochschild argues such emotional labor can lead to exhaustion and self-estrangement—that is, surface actors erect a bulwark against anger, fear, and stress, but that disconnect estranges them from the emotions that allow them to connect with others and live fulfilling lives.</p>
Don't fake it till you make it<p>Most studies on emotional labor have focused on customer service for the obvious reason that such jobs prescribe emotional states—service with a smile or, if you're in the bouncing business, a scowl. But <a href="https://eller.arizona.edu/people/allison-s-gabriel" target="_blank">Allison Gabriel</a>, associate professor of management and organizations at the University of Arizona's Eller College of Management, wanted to explore how employees used emotional labor strategies in their intra-office interactions and which strategies proved most beneficial.</p><p>"What we wanted to know is whether people choose to engage in emotion regulation when interacting with their co-workers, why they choose to regulate their emotions if there is no formal rule requiring them to do so, and what benefits, if any, they get out of this effort," Gabriel said in <a href="https://www.sciencedaily.com/releases/2020/01/200117162703.htm" target="_blank">a press release</a>.</p><p>Across three studies, she and her colleagues surveyed more than 2,500 full-time employees on their emotional regulation with coworkers. The survey asked participants to agree or disagree with statements such as "I try to experience the emotions that I show to my coworkers" or "I fake a good mood when interacting with my coworkers." Other statements gauged the outcomes of such strategies—for example, "I feel emotionally drained at work." Participants were drawn from industries as varied as education, engineering, and financial services.</p><p>The results, <a href="https://psycnet.apa.org/doiLanding?doi=10.1037%2Fapl0000473" target="_blank" rel="noopener noreferrer">published in the Journal of Applied Psychology</a>, revealed four different emotional strategies. "Deep actors" engaged in high levels of deep acting; "low actors" leaned more heavily on surface acting. Meanwhile, "non-actors" engaged in negligible amounts of emotional labor, while "regulators" switched between both. The survey also revealed two drivers for such strategies: prosocial and impression management motives. The former aimed to cultivate positive relationships, the latter to present a positive front.</p><p>The researchers found deep actors were driven by prosocial motives and enjoyed advantages from their strategy of choice. These actors reported lower levels of fatigue, fewer feelings of inauthenticity, improved coworker trust, and advanced progress toward career goals. </p><p>As Gabriel told <a href="https://www.psypost.org/2021/01/new-psychology-research-suggests-deep-acting-can-reduce-fatigue-and-improve-your-work-life-59081" target="_blank" rel="noopener noreferrer">PsyPost in an interview</a>: "So, it's a win-win-win in terms of feeling good, performing well, and having positive coworker interactions."</p><p>Non-actors did not report the emotional exhaustion of their low-actor peers, but they also didn't enjoy the social gains of the deep actors. Finally, the regulators showed that the flip-flopping between surface and deep acting drained emotional reserves and strained office relationships.</p><p>"I think the 'fake it until you make it' idea suggests a survival tactic at work," Gabriel noted. "Maybe plastering on a smile to simply get out of an interaction is easier in the short run, but long term, it will undermine efforts to improve your health and the relationships you have at work. </p><p>"It all boils down to, 'Let's be nice to each other.' Not only will people feel better, but people's performance and social relationships can also improve."</p>
You'll be glad ya' decided to smile<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="88a0a6a8d1c1abfcf7b1aca8e71247c6"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/QOSgpq9EGSw?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>But as with any research that relies on self-reported data, there are confounders here to untangle. Even during anonymous studies, participants may select socially acceptable answers over honest ones. They may further interpret their goal progress and coworker interactions more favorably than is accurate. And certain work conditions may not produce the same effects, such as toxic work environments or those that require employees to project negative emotions.</p><p>There also remains the question of the causal mechanism. If surface acting—or switching between surface and deep acting—is more mentally taxing than genuinely feeling an emotion, then what physiological process causes this fatigue? <a href="https://www.frontiersin.org/articles/10.3389/fnhum.2019.00151/full" target="_blank">One study published in the <em>Frontiers in Human Neuroscience</em></a><em> </em>measured hemoglobin density in participants' brains using an fNIRS while they expressed emotions facially. The researchers found no significant difference in energy consumed in the prefrontal cortex by those asked to deep act or surface act (though, this study too is limited by a lack of real-life task).<br></p><p>With that said, Gabriel's studies reinforce much of the current research on emotional labor. <a href="https://journals.sagepub.com/doi/abs/10.1177/2041386611417746" target="_blank">A 2011 meta-analysis</a> found that "discordant emotional labor states" (read: surface acting) were associated with harmful effects on well-being and performance. The analysis found no such consequences for deep acting. <a href="https://doi.apa.org/doiLanding?doi=10.1037%2Fa0022876" target="_blank" rel="noopener noreferrer">Another meta-analysis</a> found an association between surface acting and impaired well-being, job attitudes, and performance outcomes. Conversely, deep acting was associated with improved emotional performance.</p><p>So, although there's still much to learn on the emotional labor front, it seems Van Dyke's advice to a Leigh was half correct. We should put on a happy face, but it will <a href="https://bigthink.com/design-for-good/everything-you-should-know-about-happiness-in-one-infographic" target="_self">only help if we can feel it</a>.</p>
Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.
- Archaeologists find a cave painting of a wild pig that is at least 45,500 years old.
- The painting is the earliest known work of representational art.
- The discovery was made in a remote valley on the Indonesian island of Sulawesi.