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While AI is an important technological development, you can also have some fun with it. So, here are ten fun AI tools you should check out. 
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Find out how AI can help you in everyday tedious processes, speed up your results and become your best personal assistant, better than anybody else
Dr. Russ Conrath's insight:
Find out how AI can help you in everyday tedious processes, speed up your results and become your best personal assistant, better than anybody else
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5 FREE AI TOOLS TO MAKE YOUR CLASSROOM MORE ACCESSIBLE
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TestComplete Makes UI Testing Easy with Self-Healing Tests
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An artificial intelligence system trained on words and sentences alone will never approximate human understanding. Merijn Hos for Noema Magazine BY JACOB BROWNING AND YANN LECUNAUGUST 23, 2022 Jacob Browning is a postdoc in NYU’s Department of Computer Science working on the philosophy of AI. Yann LeCun is a Turing Award-winning machine learning researcher and an NYU Silver professor. When a Google engineer recently declared Google’s AI chatbot a person, pandemonium ensued. The chatbot, LaMDA, is a large language model (LLM) that is designed to predict the likely next words to whatever lines of text it is given. Since many conversations are somewhat predictable, these systems can infer how to keep a conversation going productively. LaMDA did this so impressively that the engineer, Blake Lemoine, began to wonder about whether there was a ghost in the machine. Reactions to Lemoine’s story spanned the gamut: some people scoffed at the mere idea that a machine could ever be a person. Others suggested that this LLM isn’t a person, but the next perhaps might be. Still others pointed out that deceiving humans isn’t very challenging; we see saints in toast, after all. But the diversity of responses highlights a deeper problem: as these LLMs become more common and powerful, there seems to be less and less agreement over how we should understand them. These systems have bested many “common sense” linguistic reasoning benchmarks over the years, many which promised to be conquerable only by a machine that “is thinking in the full-bodied sense we usually reserve for people.” Yet these systems rarely seem to have the common sense promised when they defeat the test and are usually still prone to blatant nonsense, non sequiturs and dangerous advice. This leads to a troubling question: how can these systems be so smart, yet also seem so limited? The underlying problem isn’t the AI. The problem is the limited nature of language. Once we abandon old assumptions about the connection between thought and language, it is clear that these systems are doomed to a shallow understanding that will never approximate the full-bodied thinking we see in humans. In short, despite being among the most impressive AI systems on the planet, these AI systems will never be much like us. Saying It AllA dominant theme for much of the 19th and 20th century in philosophy and science was that knowledge just is linguistic — that knowing something simply means thinking the right sentence and grasping how it connects to other sentences in a big web of all the true claims we know. The ideal form of language, by this logic, would be a purely formal, logical-mathematical one composed of arbitrary symbols connected by strict rules of inference, but natural language could serve as well if you took the extra effort to clear up ambiguities and imprecisions. As Wittgenstein put it, “The totality of true propositions is the whole of natural science.” This position was so established in the 20th century that psychological findings of cognitive maps and mental images were controversial, with many arguing that, despite appearances, these must be linguistic at base. This view is still assumed by some overeducated, intellectual types: everything which can be known can be contained in an encyclopedia, so just reading everything might give us a comprehensive knowledge of everything. It also motivated a lot of the early work in Symbolic AI, where symbol manipulation — arbitrary symbols being bound together in different ways according to logical rules — was the default paradigm. For these researchers, an AI’s knowledge consisted of a massive database of true sentences logically connected with one another by hand, and an AI system counted as intelligent if it spit out the right sentence at the right time — that is, if it manipulated symbols in the appropriate way. This notion is what underlies the Turing test: if a machine says everything it’s supposed to say, that means it knows what it’s talking about, since knowing the right sentences and when to deploy them exhausts knowledge. Related ArticlesDeep Learning Alone Isn’t Getting Us To Human-Like AI What AI Can Tell Us About Intelligence But this was subject to a withering critique which has dogged it ever since: just because a machine can talk about anything, that doesn’t mean it understands what it is talking about. This is because language doesn’t exhaust knowledge; on the contrary, it is only a highly specific, and deeply limited, kind of knowledge representation. All language — whether a programming language, a symbolic logic or a spoken language — turns on a specific type of representational schema; it excels at expressing discrete objects and properties and the relationships between them at an extremely high level of abstraction. But there is a massive difference between reading a musical score and listening to a recording of the music, and a further difference from having the skill to play it. All representational schemas involve a compression of information about something, but what gets left in and left out in the compression varies. The representational schema of language struggles with more concrete information, such as describing irregular shapes, the motion of objects, the functioning of a complex mechanism or the nuanced brushwork of a painting — much less the finicky, context-specific movements needed for surfing a wave. But there are nonlinguistic representational schemes which can express this information in an accessible way: iconic knowledge, which involves things like images, recordings, graphs and maps; and the distributed knowledge found in trained neural networks — what we often call know-how and muscle memory. Each scheme expresses some information easily even while finding other information hard — or even impossible — to represent: what does “Either Picasso or Twombly” look like? The Limits Of LanguageOne way of grasping what is distinctive about the linguistic representational schema — and how it is limited — is recognizing how littleinformation it passes along on its own. Language is a very low-bandwidth method for transmitting information: isolated words or sentences, shorn of context, convey little. Moreover, because of the sheer number of homonyms and pronouns, many sentences are deeply ambiguous: does “the box was in the pen” refer to an ink pen or a playpen? As Chomsky and his acolytes have pointed out for decades, language is just not a clear and unambiguous vehicle for clear communication. But humans don’t need a perfect vehicle for communication because we share a nonlinguistic understanding. Our understanding of a sentence often depends on our deeper understanding of the contexts in which this kind of sentence shows up, allowing us to infer what it is trying to say. This is obvious in conversation, since we are often talking about something directly in front of us, such as a football game, or communicating about some clear objective given the social roles at play in a situation, such as ordering food from a waiter. But the same holds in reading passages — a lesson which not only undermines common-sense language tests in AI but also a popular method of teaching context-free reading comprehension skills to children. This method focuses on using generalized reading comprehension strategies to understand a text — but research suggests that the amount of background knowledge a child has on the topic is actually the key factor for comprehension. Understanding a sentence or passage depends on an underlying grasp of what the topic is about.
“It is clear that these systems are doomed to a shallow understanding that will never approximate the full-bodied thinking we see in humans.” The inherently contextual nature of words and sentences is at the heart of how LLMs work. Neural nets in general represent knowledge as know-how, the skillful ability to grasp highly context-sensitive patterns and find regularities — both concrete and abstract — necessary for handling inputs in nuanced ways that are narrowly tailored to their task. In LLMs, this involves the system discerning patterns at multiple levels in existing texts, seeing both how individual words are connected in the passage but also how the sentences all hang together within the larger passage which frames them. The result is that its grasp of language is ineliminably contextual; every word is understood not on its dictionary meaning but in terms of the role it plays in a diverse collection of sentences. Since many words — think “carburetor,” “menu,” “debugging” or “electron” — are almost exclusively used in specific fields, even an isolated sentence with one of these words carries its context on its sleeve. In short, LLMs are trained to pick up on the background knowledge for each sentence, looking to the surrounding words and sentences to piece together what is going on. This allows them to take an infinite possibility of different sentences or phrases as input and come up with plausible (though hardly flawless) ways to continue the conversation or fill in the rest of the passage. A system trained on passages written by humans, often conversing with each other, should come up with the general understanding necessary for compelling conversation. Shallow UnderstandingWhile some balk at using the term “understanding” in this context or calling LLMs “intelligent,” it isn’t clear what semantic gatekeeping is buying anyone these days. But critics are right to accuse these systems of being engaged in a kind of mimicry. This is because LLMs’ understanding of language, while impressive, is shallow. This kind of shallow understanding is familiar; classrooms are filled with jargon-spouting students who don’t know what they’re talking about — effectively engaged in a mimicry of their professors or the texts they are reading. This is just part of life; we often don’t know how little we know, especially when it comes to knowledge acquired from language. LLMs have acquired this kind of shallow understanding about everything. A system like GPT-3 is trained by masking the future words in a sentence or passage and forcing the machine to guess what word is most likely, then being corrected for bad guesses. The system eventually gets proficient at guessing the most likely words, making them an effective predictive system. This brings with it some genuine understanding: for any question or puzzle, there are usually only a few right answers but an infinite number of wrong answers. This forces the system to learn language-specific skills, such as explaining a joke, solving a word problem or figuring out a logic puzzle, in order to regularly predict the right answer on these types of questions. These skills, and the connected knowledge, allow the machine to explain how something complicated works, simplify difficult concepts, rephrase and retell stories, along with a host of other language-dependent abilities. Instead of a massive database of sentences linked by logical rules, as Symbolic AI assumed, the knowledge is represented as context-sensitive know-how for coming up with a plausible sentence given the prior line. “Abandoning the view that all knowledge is linguistic permits us to realize how much of our knowledge is nonlinguistic.” But the ability to explain a concept linguistically is different from the ability to use it practically. The system can explain how to perform long division without being able to perform it or explain what words are offensive and should not be said while then blithely going on to say them. The contextual knowledge is embedded in one form — the capacity to rattle off linguistic knowledge — but is not embedded in another form — as skillful know-how for how to do things like being empathetic or handling a difficult issue sensitively. The latter kind of know-how is essential to language users, but that doesn’t make them linguistic skills — the linguistic component is incidental, not the main thing. This applies to many concepts, even those learned from lectures and books: while science classes do have a lecture component, students are graded primarily based on their lab work. Outside the humanities especially, being able to talk about something is often less useful or important than the nitty-gritty skills needed to get things to work right. Once we scratch beneath the surface, it is easier to see how limited these systems really are: they have the attention span and memory of roughly a paragraph. This can easily be missed if we engage in a conversation because we tend to focus on just the last comment or two and focus only on our next response. But the know-how for more complex conversations — active listening, recall and revisiting prior comments, sticking to a topic to make a specific point while fending off distractors, and so on — all require more attention and memory than the system possesses. This reduces even further what kind of understanding is available to them: it is easy to trick them simply by being inconsistent every few minutes, changing languages or gaslighting the system. If it is too many steps back, the system will just start over, accepting your new views as consistent with older comments, switching languages with you or acknowledging it believes whatever you said. The understanding necessary for developing a coherent view of the world is far beyond their ken. Beyond LanguageAbandoning the view that all knowledge is linguistic permits us to realize how much of our knowledge is nonlinguistic. While books contain a lot of information we can decompress and use, so do many other objects: IKEA instructions don’t even bother writing out instructions alongside its drawings; AI researchers often look at the diagrams in a paper first, grasp the network architecture and only then glance through the text; visitors can navigate NYC by following the red or green lines on a map. This goes beyond simple icons, graphs and maps. Humans learn a lot directly from exploring the world, which shows us how objects and people can and cannot behave. The structures of artifacts and the human environment convey a lot of information intuitively: doorknobs are at hand height, hammers have soft grips and so on. Nonlinguistic mental simulation, in animals and humans, is common and useful for planning out scenarios and can be used to craft, or reverse-engineer, artifacts. Similarly, social customs and rituals can convey all kinds of skills to the next generation through imitation, extending from preparing foods and medicines to maintaining the peace at times of tension. Much of our cultural knowledge is iconic or in the form of precise movements passed on from skilled practitioner to apprentice. These nuanced patterns of information are hard to express and convey in language but are still accessible to others. This is also the precise kind of context-sensitive information that neural networks excel at picking up and perfecting. “A system trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe.” Language is important because it can convey a lot of information in a small format and, especially after the creation of the printing press and the internet, can involve reproducing and making it available widely. But compressing information in language isn’t cost-free: it takes a lot of effort to decode a dense passage. Humanities classes may require a lot of reading out of class, but a good chunk of class time is still spent going over difficult passages. Building a deep understanding is time-consuming and exhaustive, however the information is provided. This explains why a machine trained on language can know so much and yet so little. It is acquiring a small part of human knowledge through a tiny bottleneck. But that small part of human knowledge can be about anything, whether it be love or astrophysics. It is thus a bit akin to a mirror: it gives the illusion of depth and can reflect almost anything, but it is only a centimeter thick. If we try to explore its depths, we bump our heads. Exorcising The GhostThis doesn’t make these machines stupid, but it also suggests there are intrinsic limits concerning how smart they can be. A system trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe. This is just the wrong kind of knowledge for developing awareness or being a person. But they will undoubtedly seem to approximate it if we stick to the surface. And, in many cases, the surface is enough; few of us really apply the Turing test to other people, aggressively querying the depth of their understanding and forcing them to do multidigit multiplication problems. Most talk is small talk. But we should not confuse the shallow understanding LLMs possess for the deep understanding humans acquire from watching the spectacle of the world, exploring it, experimenting in it and interacting with culture and other people. Language may be a helpful component which extends our understanding of the world, but language doesn’t exhaust intelligence, as is evident from many species, such as corvids, octopi and primates. Rather, the deep nonlinguistic understanding is the ground that makes language useful; it’s because we possess a deep understanding of the world that we can quickly understand what other people are talking about. This broader, context-sensitive kind of learning and know-how is the more basic and ancient kind of knowledge, one which underlies the emergence of sentience in embodied critters and makes it possible to survive and flourish. It is also the more essential task that AI researchers are focusing on when searching for common sense in AI, rather than this linguistic stuff. LLMs have no stable body or abiding world to be sentient of—so their knowledge begins and ends with more words and their common-sense is always skin-deep. The goal is for AI systems to focus on the world being talked about, not the words themselves — but LLMs don’t grasp the distinction. There is no way to approximate this deep understanding solely through language; it’s just the wrong kind of thing. Dealing with LLMs at any length makes apparent just how little can be known from language alone Via Charles Tiayon
Charles Tiayon's curator insight,
August 24, 2022 12:50 AM
"Beyond Language Abandoning the view that all knowledge is linguistic permits us to realize how much of our knowledge is nonlinguistic. While books contain a lot of information we can decompress and use, so do many other objects: IKEA instructions don’t even bother writing out instructions alongside its drawings; AI researchers often look at the diagrams in a paper first, grasp the network architecture and only then glance through the text; visitors can navigate NYC by following the red or green lines on a map. This goes beyond simple icons, graphs and maps. Humans learn a lot directly from exploring the world, which shows us how objects and people can and cannot behave. The structures of artifacts and the human environment convey a lot of information intuitively: doorknobs are at hand height, hammers have soft grips and so on. Nonlinguistic mental simulation, in animals and humans, is common and useful for planning out scenarios and can be used to craft, or reverse-engineer, artifacts. Similarly, social customs and rituals can convey all kinds of skills to the next generation through imitation, extending from preparing foods and medicines to maintaining the peace at times of tension. Much of our cultural knowledge is iconic or in the form of precise movements passed on from skilled practitioner to apprentice. These nuanced patterns of information are hard to express and convey in language but are still accessible to others. This is also the precise kind of context-sensitive information that neural networks excel at picking up and perfecting. “A system trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe.” Language is important because it can convey a lot of information in a small format and, especially after the creation of the printing press and the internet, can involve reproducing and making it available widely. But compressing information in language isn’t cost-free: it takes a lot of effort to decode a dense passage. Humanities classes may require a lot of reading out of class, but a good chunk of class time is still spent going over difficult passages. Building a deep understanding is time-consuming and exhaustive, however the information is provided. This explains why a machine trained on language can know so much and yet so little. It is acquiring a small part of human knowledge through a tiny bottleneck. But that small part of human knowledge can be about anything, whether it be love or astrophysics. It is thus a bit akin to a mirror: it gives the illusion of depth and can reflect almost anything, but it is only a centimeter thick. If we try to explore its depths, we bump our heads. Exorcising The GhostThis doesn’t make these machines stupid, but it also suggests there are intrinsic limits concerning how smart they can be. A system trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe. This is just the wrong kind of knowledge for developing awareness or being a person. But they will undoubtedly seem to approximate it if we stick to the surface. And, in many cases, the surface is enough; few of us really apply the Turing test to other people, aggressively querying the depth of their understanding and forcing them to do multidigit multiplication problems. Most talk is small talk. But we should not confuse the shallow understanding LLMs possess for the deep understanding humans acquire from watching the spectacle of the world, exploring it, experimenting in it and interacting with culture and other people. Language may be a helpful component which extends our understanding of the world, but language doesn’t exhaust intelligence, as is evident from many species, such as corvids, octopi and primates. Rather, the deep nonlinguistic understanding is the ground that makes language useful; it’s because we possess a deep understanding of the world that we can quickly understand what other people are talking about. This broader, context-sensitive kind of learning and know-how is the more basic and ancient kind of knowledge, one which underlies the emergence of sentience in embodied critters and makes it possible to survive and flourish. It is also the more essential task that AI researchers are focusing on when searching for common sense in AI, rather than this linguistic stuff. LLMs have no stable body or abiding world to be sentient of—so their knowledge begins and ends with more words and their common-sense is always skin-deep. The goal is for AI systems to focus on the world being talked about, not the words themselves — but LLMs don’t grasp the distinction. There is no way to approximate this deep understanding solely through language; it’s just the wrong kind of thing. Dealing with LLMs at any length makes apparent just how little can be known from language alone." #metaglossia mundus
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1. New AI Applications and Concerns of the Medical CommunityOne of the fastest growing trends in health information technology: Recent years have seen the rapid development of artificial intelligence (AI) technology, and the trend will continue in 2023. Standing among many industries that benefit from AI, medicine mainly applies it to profound diagnostics and detection of diseases, but it’s not limited to them. For example, IBM Watson is one of the AI platforms already available for business and healthcare (including custom medical software solutions). Let’s see what support AI can offer healthcare and associated industries and how it could become the major healthtech trend in the future. 2. Data Breach PreventionDespite all the tech precautions and healthcare provider awareness, data breach statistics demonstrate a dramatic increase over the past ten years, with violations reaching their peak in 2020/2021. These data breaches affect thousands of patients across the US. Hopefully, in 2022, healthcare providers pay more attention to their digital ecosystems and data protection. Cybersecurity in healthcare is becoming a hot technology trend for this decade. 3. NanomedicineThis may still sound like sci-fi, but nanotech is slowly entering our daily life. By the end of 2021, fantastic news spread around the globe: scientists have created tiny organic robots (so-called xenobots) that are able to self-replicate. So it’s safe to assume that 2023 can bring a bunch of revolutionary tidings in the field of nanomedicine. The nanomedicine industry offers enormous potential and welcomes early investors. If you’re wondering what nanomedicine is, here is a short definition: it’s all about the use of nanoscale (microscopically tiny) materials and objects, such as biocompatible nanoparticles, nanoelectronic devices, or even nanorobots (wow!) for specific medical purposes and manipulations, such as diagnosis or treatment of living organisms. For example, it can be used as a potential hunter for cancer cells or viruses, which requires a group of nanorobots to be injected into a human’s blood vessels. This technology is expected to successfully fight back many genetic, oncologic, or auto-immune diseases on a cellular level, including tumors, arthritis, and others (or even become an ultimate solution to them). 4. Internet of Medical Things (IoMT)Although the IoMT is not a new thing in 2023, this sector will grow exponentially in the coming years. This industry involves plenty of digital health trends, and each of them offers excellent uses to healthcare specialists, with $ billions saved in return. There are many companies providing IoMT solutions, including TATEEDA GLOBAL, which can help you design and tune your IoMT system with the help of sophisticated custom software. If you’re looking for a partner in developing custom IoMT solutions, please feel free to get in touch with us! Wearables and Mobile Apps in Medical PracticeRemote health monitoring and wellness apps are on the rise and will keep booming in 2023. If you visit GooglePlay or iTunes catalogs, you’ll find a good few professional (and myriads of semi-professional) healthcare and wellness mobile apps. Some of those mobile apps can synchronize with wearables, such as pulsometers or fitness trackers, to use data collected through the sensors placed on your body to report or analyze your health conditions, such as pulse, body temperature, blood pressure, and other metrics. TATEEDA GLOBAL, for example, has recently created an iOS/Android tablet application that provides physicians with instant access to ECG data and reports generated by devices with superior arrhythmia detection. If you have healthcare challenges that a mobile app can resolve, you can hire us to undertake full-cycle custom healthcare app development for you. 5. Social Determinants of Health Gain ValueWhen making risk assessments and compiling disease statistics, healthcare systems mainly focus on factors within their area of expertise: quality and affordability of medical services. Still, those factors are only the tip of the iceberg. Many other less apparent factors affect patients before they experience symptoms and turn to clinicians. Initially, health issues emerge due to reasons other than lack of treatment. Their roots go deeper; to demographic, environmental, and socioeconomic areas, which are rarely considered in the framework of traditional clinical diagnostics. Medical institutions mainly manage symptoms and provide recommendations on lifestyle changes, influencing treatment outcomes by as low as 10%-20%. At the same time, non-medical factors predetermine health outcomes by 80%-90%. These factors are called the social determinants of health (SDOH). In 2023, healthcare providers will approach SDOH with greater attention than ever before and start to evaluate patients’ medical histories more comprehensively, taking into consideration factors that remained unattended in previous years. 6. Smart ImplantsIn 2023, more implant-related choices and technologies will enter the healthcare market in the United States and worldwide. This promises exceptionally higher efficiency of regenerative medicine, patient rehabilitation, and a cure for many types of disabilities that have previously been considered incurable. 7. Integration of Healthcare Systems with Big Data and Data SilosThe amount of healthcare data accumulated (including patient records, DICOM files, and medical IoT solutions) and the number of data sources used by healthcare organizations will explode rapidly. Medical service providers will look for modern platforms, including data fabrics, to combine and manage huge volumes of structured and distributed data. 8. Payer-Provider Bonds Will Strengthen to the Patient’s BenefitOne of the trends in healthcare IT that shows great promise. It is not uncommon for healthcare providers and payers to have conflicting interests. When both parties adopt categorical stances, the quality of their joint work suffers. As a result, patients do not receive the services they require. They pay more, wait longer, and are often treated poorly. Providers and payers need to adopt a value-oriented approach and strive for joint achievements rather than personal gains. All need to recognize that they have the same objective, and if either party bears losses, it alienates them from the end goal – delivery of upscale medical services to citizens. 9. Universal Adoption of TelehealthThe broad diversity, universality, and increase in digitized communication channels have begun to affect the healthcare industry. Telehealth has emerged as a new means of transmitting medical information. It involves using the Internet, videoconferencing, streaming services, and other communication technologies for the remote provision of healthcare services. Telehealth also encompasses long-distance education for patients and medical specialists. In 2021, telehealth has gained universal recognition and become standard practice. Advanced clinics are already virtually consulting their patients. This type of communication will gain absolute regulatory approval and displace traditional in-house consultations in the coming years. 10. VR, Augmented, and Mixed Reality in HealthcareOne of the latest trends in healthcare information technology: Computer-generated or augmented reality promises tremendous improvements to medical diagnosis and education. Augmented Medical Education and Decision-makingWith virtual reality solutions, a person is placed in computer-rendered or fully simulated surroundings. This can help medical students to feel integrated with virtual situations and locations, similar to what they may face in reality, and practice their skills without visiting hospitals or dealing with actual patients. With augmented reality solutions, a computer-rendered layer of additional information or virtual objects is added to the real world. Students or care providers can use augmented reality to access information and reports while working with patients or without leaving their current operations, in a hands-free mode, via voice command, or have supportive data appear automatically.
bwell's curator insight,
April 18, 2023 8:50 AM
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Welcome to a workshop arranged by the ASG Empathy and Compassion at the Pufendorf IAS, Lund University.
Via Edwin Rutsch
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From robots to home-based care models, the past year’s wave of innovation is poised to continue.
A strong, technology-driven response to address urgent needs will have positive implications that last beyond the current health crisis, giving patients and providers new options for preventive care and better connectivity.
Rapid advancements, though exciting, can challenge IT teams, who carry the duty of knowing what to deploy and incorporating it into their clinical ecosystems.
“Doing so is critical to help guide organizations through cultural and behavioral adaptation at a pace that’s not normal.” With COVID-19 vaccines on the way and patient expectations quickly shifting, there will be plenty to consider in 2021. Here are 5 things teams should keep in mind:
Organizations are supporting this movement by optimizing and expanding their telehealth programs and setting up digital portals that offer a variety of self-service functions and messaging services.
Efforts to curb readmissions, which have taken on higher importance in recent months, are being bolstered by health IT teams launching and expanding wearables and remote patient monitoring programs to collect and transmit patients’ vital signs from afar. Providers, though, must be ready to handle issues of education and connectivity.
Deployments may include autonomous robots that emit germ-killing ultraviolet light to decontaminate rooms in 15 minutes and RFID technology to track how long — and how often — employees wash their hands. More hospitals are using thermal cameras at entryways to detect those with elevated body temperature, a common but not universal symptom of COVID-19.
Expect to see more design changes to buildings. These include convertible spaces to accommodate temporary surges in critical-care patients, transparent glass or plastic walls to view isolated patients, retrofitted rooms for delivering inpatient telehealth and tools such as touch-screen kiosks and handheld alert buzzers so people won’t crowd a waiting area before a visit.
3. AI and Automation for Efficiency, Reduced Clinical Burden
The need for efficiency and touch-free interactions has the potential to boost clinical use of natural language processing — a branch of AI that allows computers to understand spoken remarks — by seamlessly transmitting data into a patient’s electronic health record. Alternatively, automated services such as symptom-checking chatbots will continue to ease administrative bottlenecks.
4. Augmented and Virtual Reality Integration Across the Care Spectrum Being immersed in a virtual world or viewing real-life spaces with digital enhancements is no longer just a game. With the help of headsets and specially designed software, medical professionals are finding wider uses for augmented and virtual reality. The closure of classroom and clinical spaces during the pandemic underscores the potential.
Options include lifelike surgical training programs, supplemental clinical experiences for nursing students, distraction for pain management and even the ability to view images with a new and detailed perspective (clinicians at the George Washington University Hospital, for instance, recently used VR to analyze the lung scans of a COVID-19 patient).
VR also is also poised to gain traction in senior care communities. Although activities such as virtual travel and avatar-led chat rooms might seem like fun, the engaging and memory-triggering encounters can also be highly therapeutic. The technology, which can be used solo or in a group, opens up a new world for older adults during quarantine.
Likewise, increased data interoperability between different EHR platforms and healthcare systems will be critical in tracking who has been vaccinated. This will be key for general public health, of course, but also in ensuring accurate record-keeping if a person relocates or changes providers before the second vaccination.
New federal interoperability rules designed to improve consumers’ access to their own health data will likely aid these efforts, but organizations will be required to adopt new technologies and data-sharing standards to make it happen.
bwell's curator insight,
April 18, 2023 9:00 AM
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In a recent interview Noam Chomsky shares his thoughts on ChatGPT and why he feels that its a wakeup call for our educational model. In a recent interview, renowned linguist and cognitive scientist Noam Chomsky gave his thoughts on the rise of ChatGPT, and its effect on education. What he had to say wasn't favorable. As more and more educators struggle with how to combat plagiarism and the use of these chatbots in the classroom, Chomsky gives a clear viewpoint. For him, the key all lies in how students are taught, and, currently, our educational system is pushing students toward ChatGPT and other shortcuts. “I don’t think [ChatGPT] has anything to do with education,” Chomsky tells interviewer Thijmen Sprakel of EduKitchen. “I think it’s undermining it. ChatGPT is basically high-tech plagiarism.” The challenge for educators, according to Chomsky, is to create interest in the topics that they teach so that students will be motivated to learn, rather than trying to avoid doing the work. Chomsky, who spent a large part of his career teaching at MIT, felt strongly that his students wouldn't have turned to AI to complete their coursework because they were invested in the material. If students are relying on ChatGPT, Chomsky says it’s “a sign that the educational system is failing. If students aren’t interested, they’ll find a way around it.” The American intellectual strongly feels like the current educational model of “teaching to test” has created an environment where students are bored. In turn, the boredom turns to avoidance, and ChatGPT becomes an easy way to avoid the education. While some argue that chatbots like ChatGPT can be a useful educational tool, Chomsky has a much different opinion. He feels that these natural language systems “may be of value for some things, but it's not obvious what.” Meanwhile, it appears that schools are scrambling to figure out how to counteract the use of ChatGPT. Many schools have banned ChatGPT on school devices and networks, and educators are adjusting their teaching styles. Some are turning to more in-class essays, while others are looking at how they can incorporate the technology into the classroom. It will be interesting to see if the rise of chatbots helps steer us toward a new teaching philosophy and away from the “teaching to test” method that has become the driving force of modern education. It's the kind of education that Chomsky says was “ridiculed during the Enlightenment,” and so indirectly, this new technology may force schools to rethink how they ask students to apply their knowledge. Listen to Noam Chomsky speak about the rise of ChatGPT in education.h/t: [Open Culture] Related Articles:Ordinary Photos of a House Party Are Actually an AI-Generated Event AI-Generated Art Imagines a Fabulous Fashion Show Featuring Only Senior Models AI Chatbots Now Let You Talk to Historical Figures Like Shakespeare and Andy Warhol
JESSICA STEWARTJessica Stewart is a Contributing Writer and Digital Media Specialist for My Modern Met, as well as a curator and art historian. Since 2020, she is also one of the co-hosts of the My Modern Met Top Artist Podcast. She earned her MA in Renaissance Studies from University College London and now lives in Rome, Italy. She cultivated expertise in street art which led to the purchase of her photographic archive by the Treccani Italian Encyclopedia in 2014. When she’s not spending time with her three dogs, she also manages the studio of a successful street artist. In 2013, she authored the book 'Street Art Stories Roma' and most recently contributed to 'Crossroads: A Glimpse Into the Life of Alice Pasquini'. You can follow her adventures online at @romephotoblog. Via Charles Tiayon
Dr. Russ Conrath's insight:
Plagiarism or a creative tool?
Charles Tiayon's curator insight,
February 20, 2023 8:53 PM
"In a recent interview, renowned linguist and cognitive scientist Noam Chomsky gave his thoughts on the rise of ChatGPT, and its effect on education. What he had to say wasn't favorable. As more and more educators struggle with how to combat plagiarism and the use of these chatbots in the classroom, Chomsky gives a clear viewpoint. For him, the key all lies in how students are taught, and, currently, our educational system is pushing students toward ChatGPT and other shortcuts. “I don’t think [ChatGPT] has anything to do with education,” Chomsky tells interviewer Thijmen Sprakel of EduKitchen. “I think it’s undermining it. ChatGPT is basically high-tech plagiarism.” The challenge for educators, according to Chomsky, is to create interest in the topics that they teach so that students will be motivated to learn, rather than trying to avoid doing the work. Chomsky, who spent a large part of his career teaching at MIT, felt strongly that his students wouldn't have turned to AI to complete their coursework because they were invested in the material. If students are relying on ChatGPT, Chomsky says it’s “a sign that the educational system is failing. If students aren’t interested, they’ll find a way around it.” The American intellectual strongly feels like the current educational model of “teaching to test” has created an environment where students are bored. In turn, the boredom turns to avoidance, and ChatGPT becomes an easy way to avoid the education. While some argue that chatbots like ChatGPT can be a useful educational tool, Chomsky has a much different opinion. He feels that these natural language systems “may be of value for some things, but it's not obvious what.” Meanwhile, it appears that schools are scrambling to figure out how to counteract the use of ChatGPT. Many schools have banned ChatGPT on school devices and networks, and educators are adjusting their teaching styles. Some are turning to more in-class essays, while others are looking at how they can incorporate the technology into the classroom. It will be interesting to see if the rise of chatbots helps steer us toward a new teaching philosophy and away from the “teaching to test” method that has become the driving force of modern education. It's the kind of education that Chomsky says was “ridiculed during the Enlightenment,” and so indirectly, this new technology may force schools to rethink how they ask students to apply their knowledge" #metaglossia mundus
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University College Dublin Library's Special Collections contains unique book, archival and manuscript collections. This channel highlights some of thes Via Gerard Beirne
Dr. Russ Conrath's insight:
Library Poetry Reading from Dublin. Maybe more librarians should read poetry to remind students and others how poetry is overlooked, and yet still relevant for today's human experience.
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LibGuides: Basic & Transitional Studies/TRiO: North Seattle College Library Resources: Home...
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A regional library service that includes 18 libraries in Marion, Polk, Yamhill and Linn counties will switch to a new computer system for the first time in a decade. Via Charles Tiayon
Dr. Russ Conrath's insight:
"Eight things library users should know."
|
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Artificial intelligence might conjure visions of robots that take over the world. And while that is a form of artificial intelligence, AI means so much more -- and is probably used in devices and tools you use every day. Watch to learn how AI has proliferated almost every enterprise and how it affects your daily life.
Dr. Russ Conrath's insight:
"Artificial intelligence might conjure visions of robots that take over the world. And while that is a form of artificial intelligence, AI means so much more -- and is probably used in devices and tools you use every day."
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Artificial Intelligence tools are becoming increasingly popular as each day passes because they can drastically reduce the amount of time and effort spent on even the most complex tasks.
Dr. Russ Conrath's insight:
AI "can drastically reduce the amount of time and effort spent on even the most complex tasks."
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Artificial intelligence (AI) technologies have opened up countless new opportunities for every size business across the globe. AI is providing deep insights like never before, and it is helping turn many business processes more efficient. Whether you are a freelancer with a one-person business or in charge of multiple employees, there are many tools that […]
Dr. Russ Conrath's insight:
Not just tools for business, but also for your classroom
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7 Awesome and Free AI Tools You Should Know
Dr. Russ Conrath's insight:
7 Awesome and Free AI Tools You Should Know
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As the digital landscape continues to evolve, so must our content marketing strategies. Sharing curated content is one of the most effective ways to maximize your reach and grow your business in 2023.
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Gust MEES's curator insight,
March 6, 2023 2:18 PM
As the digital landscape continues to evolve, so must our content marketing strategies. Sharing curated content is one of the most effective ways to maximize your reach and grow your business in 2023.
Learn more / En savoir plus / Mehr erfahren:
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https://www.scoop.it/topic/21st-century-learning-and-teaching/?&tag=Blogging
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In a recent interview Noam Chomsky shares his thoughts on ChatGPT and why he feels that its a wakeup call for our educational model. In a recent interview, renowned linguist and cognitive scientist Noam Chomsky gave his thoughts on the rise of ChatGPT, and its effect on education. What he had to say wasn't favorable. As more and more educators struggle with how to combat plagiarism and the use of these chatbots in the classroom, Chomsky gives a clear viewpoint. For him, the key all lies in how students are taught, and, currently, our educational system is pushing students toward ChatGPT and other shortcuts. “I don’t think [ChatGPT] has anything to do with education,” Chomsky tells interviewer Thijmen Sprakel of EduKitchen. “I think it’s undermining it. ChatGPT is basically high-tech plagiarism.” The challenge for educators, according to Chomsky, is to create interest in the topics that they teach so that students will be motivated to learn, rather than trying to avoid doing the work. Chomsky, who spent a large part of his career teaching at MIT, felt strongly that his students wouldn't have turned to AI to complete their coursework because they were invested in the material. If students are relying on ChatGPT, Chomsky says it’s “a sign that the educational system is failing. If students aren’t interested, they’ll find a way around it.” The American intellectual strongly feels like the current educational model of “teaching to test” has created an environment where students are bored. In turn, the boredom turns to avoidance, and ChatGPT becomes an easy way to avoid the education. While some argue that chatbots like ChatGPT can be a useful educational tool, Chomsky has a much different opinion. He feels that these natural language systems “may be of value for some things, but it's not obvious what.” Meanwhile, it appears that schools are scrambling to figure out how to counteract the use of ChatGPT. Many schools have banned ChatGPT on school devices and networks, and educators are adjusting their teaching styles. Some are turning to more in-class essays, while others are looking at how they can incorporate the technology into the classroom. It will be interesting to see if the rise of chatbots helps steer us toward a new teaching philosophy and away from the “teaching to test” method that has become the driving force of modern education. It's the kind of education that Chomsky says was “ridiculed during the Enlightenment,” and so indirectly, this new technology may force schools to rethink how they ask students to apply their knowledge. Listen to Noam Chomsky speak about the rise of ChatGPT in education.h/t: [Open Culture] Related Articles:Ordinary Photos of a House Party Are Actually an AI-Generated Event AI-Generated Art Imagines a Fabulous Fashion Show Featuring Only Senior Models AI Chatbots Now Let You Talk to Historical Figures Like Shakespeare and Andy Warhol
JESSICA STEWARTJessica Stewart is a Contributing Writer and Digital Media Specialist for My Modern Met, as well as a curator and art historian. Since 2020, she is also one of the co-hosts of the My Modern Met Top Artist Podcast. She earned her MA in Renaissance Studies from University College London and now lives in Rome, Italy. She cultivated expertise in street art which led to the purchase of her photographic archive by the Treccani Italian Encyclopedia in 2014. When she’s not spending time with her three dogs, she also manages the studio of a successful street artist. In 2013, she authored the book 'Street Art Stories Roma' and most recently contributed to 'Crossroads: A Glimpse Into the Life of Alice Pasquini'. You can follow her adventures online at @romephotoblog. Via Charles Tiayon
Charles Tiayon's curator insight,
February 20, 2023 8:53 PM
"In a recent interview, renowned linguist and cognitive scientist Noam Chomsky gave his thoughts on the rise of ChatGPT, and its effect on education. What he had to say wasn't favorable. As more and more educators struggle with how to combat plagiarism and the use of these chatbots in the classroom, Chomsky gives a clear viewpoint. For him, the key all lies in how students are taught, and, currently, our educational system is pushing students toward ChatGPT and other shortcuts. “I don’t think [ChatGPT] has anything to do with education,” Chomsky tells interviewer Thijmen Sprakel of EduKitchen. “I think it’s undermining it. ChatGPT is basically high-tech plagiarism.” The challenge for educators, according to Chomsky, is to create interest in the topics that they teach so that students will be motivated to learn, rather than trying to avoid doing the work. Chomsky, who spent a large part of his career teaching at MIT, felt strongly that his students wouldn't have turned to AI to complete their coursework because they were invested in the material. If students are relying on ChatGPT, Chomsky says it’s “a sign that the educational system is failing. If students aren’t interested, they’ll find a way around it.” The American intellectual strongly feels like the current educational model of “teaching to test” has created an environment where students are bored. In turn, the boredom turns to avoidance, and ChatGPT becomes an easy way to avoid the education. While some argue that chatbots like ChatGPT can be a useful educational tool, Chomsky has a much different opinion. He feels that these natural language systems “may be of value for some things, but it's not obvious what.” Meanwhile, it appears that schools are scrambling to figure out how to counteract the use of ChatGPT. Many schools have banned ChatGPT on school devices and networks, and educators are adjusting their teaching styles. Some are turning to more in-class essays, while others are looking at how they can incorporate the technology into the classroom. It will be interesting to see if the rise of chatbots helps steer us toward a new teaching philosophy and away from the “teaching to test” method that has become the driving force of modern education. It's the kind of education that Chomsky says was “ridiculed during the Enlightenment,” and so indirectly, this new technology may force schools to rethink how they ask students to apply their knowledge" #metaglossia mundus
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So top healthcare digital transformation trends in 2023 are as follows:
1. Internet of Medical Things (IoMT)According to Precedence Research, the global internet of things (IoT) in healthcare market size was valued at $180.5 billion in 2021 and it is expected to reach around $960.2 billion by 2030 with a compound annual growth rate (CAGR) of 20.41% during the forecast period 2022 to 2030. Wearables and trackers are a huge part of healthcare information technology development. Their key benefit is providing real-time and detailed data on the patients’ health states, which is precious for doctors’ observation. 2. Cybersecurity and Data PrivacyData security is still a key industrial concern, and cybersecurity will appear relevant among trends in the health information technology industry for a long time. All future technological enhancements will stick to the requirement to possess a significant security layer. The ultimate goal is to protect sensitive patient data delivered online. At the end of 2021, the number of weekly attacks on healthcare reached an average of 626 per organization and is constantly growing. The US Federal Bureau of Investigation stated that over 40 million patients’ healthcare records were compromised in 2021, as half of the internet-connected hospital devices are vulnerable to cyberattacks. 3. Remote Patient Monitoring (RPM)Of the latest trends in the healthcare industry, RPM occupies a special place. COVID-19 also contributed to turning remote patient monitoring into the current tendency in the medical field. The rise of virtual healthcare takes many forms, including online appointments, remote care, and video conferencing. And the wide range of IoMT devices empowers RPM with even more opportunities. According to Research and Markets, the global RPM systems market is projected to be worth over $175.2 billion by 2027, compared to $53.6 billion in 2022. 4. Improved Big Data & AnalyticsAccording to Frost & Sullivan’s forecast, introducing AI technology in healthcare data analytics can save the industry at least $150 million by 2025. Such an achievement is possible thanks to real-time and long-distance analysis and measurement of patient data. Given such striking cost-effectiveness, the AI trend will hit its stride in the next few years. For medical providers and researchers, the COVID-19 pandemic spotlighted data analytics, along with the immense challenge of real-time decision-making under rapidly changing conditions. Healthcare providers can get lost in dozens of spreadsheets and meetings if processed manually. That’s why the digital future inevitably includes technology capable of processing Big Data instead of humans and providing real-time analytics for decision-makers. 5. Cloud MigrationCloud migration in healthcare is now in full swing and will see significant growth in the future. MarketsandMarkets states that the global healthcare cloud computing market is projected to reach $89.4 billion by 2027, from an estimated $39.4 billion in 2022 at a CAGR of 17.8% during the forecast period. It solves many major challenges in service delivery, including record management, remote care, and reaching low-income patients. Providers worldwide use the cloud to efficiently manage emails and electronic medical records (EMRs) and make real-time data available to healthcare professionals. 6. Robotic Process Automation (RPA)The World Healthcare Organization (WHO) predicted that the global healthcare worker shortage could reach 12.9 million professionals by 2035, making in-person medical appointments a luxury few patients can afford. And the COVID-19 pandemic has only aggravated things. Wide-scale adoption of robotic process automation (RPA) solutions in healthcare may cure medical system inefficiencies. RPA-empowered bots are a significant milestone in healthcare information technology development. Health providers can experience accurate automation, cost reduction, staff optimization, and even introduce transformational changes with the know-how. On the patient side, AI algorithms can guide people to the needed doctor by scanning their symptoms more accurately than traditional search engines. 7. Cognitive Automation (CA)Among the emerging healthcare technology trends, it is worth highlighting CA. Cognitive Automation is the next-level tendency for true digital transformation. These days, it’s among emerging IT market trends in healthcare, but the tech package and applicability are about to change the state of managing an industry to the core. It takes the achievements of RPA but applies the mimicking of human behavior beyond the repetitive tasks. In short, it serves as the digital brain of the healthcare organization. In essence, CA takes the automation capabilities of current software providers and applies ML algorithms to introduce decision velocity in the industry. It becomes possible to process zettabytes of data within seconds and provide decision-makers with ready-to-accept recommendations backed up by real-time data. The ultimate goal is to establish a self-driving enterprise where all the operational processes are automated. 8. FinTech IntegrationHealthcare spending is likely to reach $6 trillion by 2027. Until recently, the healthcare system tended to be an old-school bureaucratic system. But now, many hospitals and medical institutions have turned to the tech industry to improve their filing and billing processes, and tech has begun delivering. The financial technology covers insurance, management services, digital payments, settlement services, capital-raising, deposits, and credit services. Thus it facilitates and streamlines healthcare processes by lowering the cost of financial services. Through robotic investment advice, P2P lending, mobile payments, artificial intelligence, machine learning, and blockchain technologies, fintech boosts the healthcare sector by mitigating inefficiencies in its payment plans. 9. Interoperability and ConnectivityIt is another healthcare tech trend that popped up during the global pandemic due to the lack of data interoperability. It has slowed caregivers down for years and impacted their ability to provide the best possible care. The interoperability and connectivity of medical devices are widely recognized as important factors in helping hospitals achieve better patient data flow, synchronization, quicker and more accurate identification of high-risk patients, and improving the overall outcome. 10. TelehealthThroughout the COVID-19 pandemic, telemedicine has seen the wildest growth and has become one of the hottest trends in the healthcare industry. 82% of customers experienced telemedicine for the first time since the start of COVID-19. According to Precedence Research, the telemedicine market is poised to grow from about $60.8 billion in 2022 to $225 billion in 2030. It has become an integral part of the whole healthcare industry. 11. Data Breach PreventionOver the years, the data breach issue has become more critical. According to the HIPAA Journal, today, the number of healthcare data breaches exceeds two times daily, which was a concern only once a day in 2017. 12. Network StrategiesThe healthcare digital transformation market is growing at over 14% annually. In conditions where doctors actively use telemedicine and patients track their progress through wearables and mHealth apps, a network strategy is necessary. Therefore, the implementation of network solutions for effective communication and data exchange between doctors, patients, and healthcare organizations is another trend in the medical field. 13. Tailored Patient ExperienceAnother prominent trend in healthcare is the provision of a tailored patient experience. It’s especially noticeable considering the global precision medicine market growth, which will reach $146.6 billion by 2028, in contrast to $65.9 billion in 2021. In the coming years, implementing any new technology in the medical field will be carried out by putting the patient at the center of this process. Studying the specifics of each patient’s condition will help draw up more effective treatment plans and get better treatment outcomes.
bwell's curator insight,
April 18, 2023 8:47 AM
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james brown's curator insight,
May 26, 2023 4:20 PM
Quando si tratta di questioni di salute, è fondamentale sapere quali sono le farmacie buone e cattive. Devi imparare cosa cercare in una farmacia per sapere se è quella giusta. Ricorda, hai a che fare con la vita, ecco perché devi stare attento a dove acquistare i tuoi farmaci e altre necessità farmaceutiche.
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Stories are a great way to resolve disputes, minimize conflict situations, and build empathy. Via Edwin Rutsch
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Tectonic shifts in society and business occur when unexpected events force widespread experimentation around a new idea. During World War II, for instance, when American men went off to war, women proved that they could do “men’s” work — and do it well. Women never looked back after that. Similarly, the Y2K problem demanded the extensive use of Indian software engineers, leading to the tripling of employment-based visas granted by the U.S. Fixing that bug enabled Indian engineers to establish their credentials, and catapulted them as world leaders in addressing technology problems. Alphabet, Microsoft, IBM, and Adobe are all headed by India-born engineers today. Via Inovação Educacional, juandoming, THE OFFICIAL ANDREASCY
Dr. Russ Conrath's curator insight,
February 14, 2023 12:23 PM
"Tectonic shifts in society and business occur when unexpected events force widespread experimentation around a new idea. During World War II, for instance, when American men went off to war, women proved that they could do “men’s” work — and do it well. Women never looked back after that. "
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What will we lose in the process? Jackson Ryan Feb. 20, 2023 4:09 p.m. PT12 min read Within a few short years, we could find ourselves living on a planet devoid of Google Search. That might seem dramatic. After all, Google Search is probably the horse you rode in on; your first step on a microsecond-long journey across the internet that brought you to this article. Maybe you were searching for "ChatGPT" or "OpenAI" or maybe you were trying to break Google by typing "Google" into Google. (It just gives you a lot of Google, don't bother.) Maybe your smartphone served you this article because you've been reading a lot about AI at CNET lately. Whatever the case, you're here now, and more often than not that's thanks to Google Search. For more than two decades, Google's empty search bar has rolled out the welcome mat to what we used to call the World Wide Web. Challengers have appeared over its 20-year dominance but not one has come close to dethroning the search king. Claims of its coming death have been made routinely and earnestly, but most contenders haven't even made it into the castle. But from the moment OpenAI's ChatGPT began algorithmically generating waves in November, something shifted. ChatGPT is a generative AI that can write human-sounding answers in response to basically any question you ask of it. Its proficiency has wowed anyone who has asked it to write code, essay answers, poetry or prose. It's so good that practically every tech expert, countless journalists and niche Substack writers began posing the question: Will ChatGPT kill Google? It wasn't just experts and writers, either. The Searchicide alarm bells began wailing across the open-plan offices at Google itself. Barely two months after ChatGPT first appeared, the tech giant initiated a "Code Red" response, upending various teams to respond to the threat the chatbot (or more accurately, its underlying AI) poses to its Search monopoly. The stakes have only become higher since Microsoft added AI assistance to Bing, its homegrown Google competitor. Artificial intelligence has long powered Google Search: Black-box algorithms rank pages and offer relevant links for users to sift through. But the generative AI tools being rolled out promise to reimagine our relationship with Search entirely. Our entry into the web — from our computer screen, from our smartphone — is morphing from a welcome mat to a red carpet. As a result, sometime in the not so distant future, we might find ourselves living on a planet without Google Search. Or, at least one without Google Search as we know it today. That is a world we don't fully understand; with consequences and possibilities we are yet to completely grasp. It's a world we're not ready for. And yet, this may very well be the world we are about to inhabit. Google search fundamentally altered the internet and the way we access information. Today, it accounts for about nine in 10 searches online and is the default on practically any internet-enabled device across most of the world. (Baidu is the most prominent search engine in China, where Google is banned.) If you want to find something on the web, Google Search is not unavoidable — but it might as well be. Need to find the definition of soliloquy? Dictionary not required; ask Google. Want to know Leonardo DiCaprio's age? That's an easy one for Google. Best restaurants nearby? Google has you. Looking for a new pair of headphones? Just Google it. Its supremacy has seen it move from a humble web crawler to a verb; an all-knowing entity in its own right. Despite its dominance, complaints about the declining quality of Google Search have been gaining traction over the last few years. "If you've tried to search for a recipe or product review recently, I don't need to tell you that Google search results have gone to shit," wrote Dmitri Brereton, a software engineer fascinated by search engines, in early 2022. Author Cory Doctorow has complained about the "enshittification" of internet services that move into the mainstream, collapsing from useful user experiences to corporate cash cows. Exhibit A: Google Search. Others have discussed Google tips and hacks tailored to refine search results, like appending "reddit" or "yelp" to a query. These additional search terms help narrow down the kind of content you're looking for, supplying you with links to specific websites. Angela Hoover, who co-founded the conversational AI search engine Andi, has two major frustrations with Google: "All the ads and the SEO spam." She notes it's those issues that led to a product with search results that "just aren't very good." These are constant bugbears in conversations I've had with other researchers studying AI and Google, too. A Google spokesperson tells CNET the company is always working to make Search better, delivering thousands of changes each year. Advertising is the most lucrative revenue stream for Alphabet, Google's parent company. According to its 2022 financial report, advertising generated $224 billion for Google, almost 80% of its total revenue for the year — and a $13.5 billion increase over 2021. Depending on your search term (and browser extensions), ads will likely flood the top half of your search. Advertisers spend big with Google because of the sheer breadth of humanity the search engine gives them access to. Its dominance is such that the Department of Justice wants Google to sell off the ad business. Enlarge Image Andisearch.com is a conversational search engine attempting to reimagine how we find information on the web. Screenshot by CNETThe SEO spam is a separate but related issue. Even if you don't know too much about SEO, or search engine optimization, you know that when you query Google you're met with a deluge of navy-blue links shouting similar-sounding headlines. If you're looking for news about Rihanna's performance and pregnancy at the Super Bowl, you'll likely find a similar series of words in each headline: "Rihanna, pregnancy, super bowl, halftime." In this way, Google has reshaped how content sounds on the internet: There's a never-ending arms race between bloggers, publishers, major news outlets, content creators and anyone who wants to sell you something to make sure their headline ranks well on Google Search. If you click through to their page, they might make a few ad dollars. For that reason, there are jobs wholly devoted to understanding how Google ranks a page and the black box algorithms that rule SEO. AI-assisted search, at least in theory, could ease these frustrations. Hoover, for instance, says that Andi does not plan to serve ads in its conversational search results, and instead hopes to sell subscriptions and an enterprise API. A suite of other alternatives such as YouChat and Neeva are attempting to shake things up in similar ways. By altering the incentives — websites no longer have to game Google, they just have to write good content that's relevant to a user's search — perhaps SEO spam can be quelled. At least for those of us willing to add yet another subscription to our monthly spending. This is an oversimplification of an expansive problem. We haven't even talked about the privacy aspects of Google Search. But there are some simple truths: We want information quickly. We want good information. We want it to be trustworthy. A world without Google Search — one dominated by conversational, question-and-answer, generative AI search engines — might provide answers more readily. But can we trust those answers? That's still up for debate. Microsoft announced its AI-assisted Bing in a splashy event at Microsoft HQ on Feb. 7. The event has been heralded as the beginning of the "Chatbot Search Wars." Bing, some believe, will finally infiltrate the Google kingdom and may even slay the final boss. In launching Bing to a select group, Microsoft volleyed the first offensive in this so-called war. Reporters who have had a chance to rummage through the new Bing have mostly praised its abilities. Our very own Stephen Shankland compared its results to traditional Google Search results and found it came out on top eight out of 10 times on some complex queries. It was able to provide suggestions for a day hike on a road trip between LA and Albuquerque, respond to news about Chinese balloons over the US and write an email apologizing for being late. The demo version impressed New York Times reporter Kevin Roose so much that he announced in his column on Feb. 9 that he would be switching his computer's default search engine to Bing. (A week later, Roose reneged on that commitment.) Browsing through the Bing subreddit and Twitter, that switch seems premature — even dangerous. Bing's search relies on the AI that underpins ChatGPT, known as a large language model. This type of AI, trained on huge swaths of human text, is able to generate sentences, paragraphs and entire essays. It makes predictions on what word or phrases should appear next, like a supercharged autocomplete tool. These predictions are based on a mathematical model then tuned by human testers. Microsoft is incorporating ChatGPT-like AI into Bing and Edge. Getty ImagesFor that reason, LLMs are prone to "hallucinating" — the term AI researchers use to describe an AI engine making things up, even when based on factual information. At CNET, we've had to wrestle with that very problem with our own AI engine, which has generated errors in financial articles. Bing's AI assistant is no different. One of the most egregious examples is when it went off-piste in response to a user query about show times for Avatar 2: The Way of Water. Not only did Bing's AI assistant get the year wrong, suggesting it was 2022, it began to take an aggressive stance with the user saying "I'm trying to be helpful, but you are not listening to me." (Brereton documented Bing's propensity for falsehoods in a blog post on Feb 14.) This isn't just a problem for Bing, either. Google unveiled Bard, its ChatGPT rival, just a day before the Microsoft event. Eagle-eyed astronomers quickly pointed out that during Google's presentation, Bard had flubbed a fact about NASA's James Webb Space Telescope. That mistake wiped a cool $100 billion from Google's market value. A Google spokesperson noted that AI experiences are not available to the public yet, and won't be released until they've met high standards for quality and safety. A Microsoft spokesperson said it recognizes "there is still work to be done and [it is] expecting that the system may make mistakes during this preview period," while pointing out that thousands of users who have interacted with the preview version of Bing and provided feedback will "help the models get better." But these errors get at the core problem with nu-Search 3.0: confident-sounding bullshit. That's somewhat baked into how the models work and it's a problem compounded by the way "search" is set to change with conversational AI. No longer will we be provided with a list of links and possible answers to sift through. Instead, AI will generate one single answer presented as an objective truth, perhaps with a handful of citations. How will this change our relationship with search and the truth? Heather Ford, head of discipline for digital and social media at the University of Sydney, has been trying to answer that question. Her team has been analyzing the way humans respond to virtual question-and-answer assistants like Siri or Alexa — more primitive versions of ChatGPT and Google's Bard. Early studies reveal a concerning trend that could become increasingly relevant as we move from old-timey Google Search to generative AI search. "When people see an automated answer or when they imagine there's some kind of automation that's going on in the background to produce an answer, they will believe that more readily than they would if a single journalist, for example, had produced the answer," she says. Ford notes that further research is required to understand this phenomenon more clearly but, generally, humans trust automation more than they trust other humans. We think automation removes bias and flaws when, in fact, the systems are biased and flawed, too. This problem is easily minimized if these products are tested and examined before being rolled out for mass use, but with the success of ChatGPT, that hasn't been the case. Both Microsoft and Google are moving faster to get AI into their products. The act of searching on Google is an artifact of the early internet. Search engines operated like digital filing cabinets. They didn't take us directly to an answer, but they put us in the right drawer. As they've evolved, they've become better at sending us on the right path — we find answers more quickly — but for a lot of questions, we're still served a handful of folders and asked to scrounge around for the answer. That's somewhat unnatural. "People aren't searching because they want links, people are searching because they want answers," says Toby Walsh, a professor of artificial intelligence at the University of New South Wales, Australia. Fundamentally, this is why ChatGPT and the new chatbot search engines are so impressive. They give us an immediate answer. Google does have this power. Facts are easily accessible and Google's knowledge panels, for the most part, provide truthful answers to common questions about people, places and things. What's different is the way they take advantage of the way we communicate with other people. Hoover, the co-founder of Andi, notes that conversational search presents a type of interaction we're more familiar with thanks to our chat apps and text messages. TikTok search is a useful tool for learning about certain experiences. James Martin/CNET"On my phone, I live in visual feeds and chat apps," she says, noting she's in Gen Z. "It just makes sense that that is part of what the future of search will look like." Those feeds and apps have already changed our relationship with search. In some ways, we've been subconsciously primed to move on from Google because we can find specific, helpful information elsewhere. Our questions are being answered by TikToks, Instagram photos and YouTube videos. Farhad Manjoo, an opinion columnist at The New York Times, argued in February there's already a better search engine than Google for certain types of queries: YouTube. "If you want to make a soufflé, fix a clogged drain, learn guitar, improve your golf swing or do essentially anything that is best understood by watching someone else do it, there is almost no point searching anywhere other than YouTube," he wrote. For me, TikTok has been an unexpected and powerful search engine. In doing research for a long-term trip to Europe, it provided rapid access to human experiences. With Google, I can read endless opinions about where the best fried chicken is or what libraries to visit. But with TikTok, I can punch in my search term and get authentic, visual guides of these places. I can set expectations in a different way. Deepfakes and AI-generated video aside, I can trust that what I see is what I get. YouTube has traded on this authenticity for years, and TikTok is now doing the same. I'm not sure that a planet without Google Search will definitely come to pass, but if it does, this fracturing of our search experience seems like one possible future scenario — at least until the artificial intelligence gets so good that it's merely serving all these results up for us to endlessly doomscroll through, one after the other. A fractured search economy, where users are bouncing across different engines and apps, is an interesting possible future. It may even be a better one. For researchers like Ford, the power behind search today lies with only a few companies, which influences the way information travels. "It's the structural dominance that is a problem," notes Ford. "We have less rich conversations in the world when we have such dominant players determining these single answers." We could, eventually, find ourselves living on a planet where Google Search doesn't exist. This is not a particularly controversial idea. It's one software engineers, tech experts and Google itself have had to contend with for years. In fact, it's so belabored that Brereton, the independent search engine researcher, notes "it's a bit of a meme that like every few years someone says that Google is dead." How soon we move on from Google, despite the rise of the chatbot search engines in the past few months, remains highly questionable. Even as nu-Search dramatically alters the way humanity accesses information, it feels premature to suggest that any of these AI tools are ready for primetime. Yet they're out there. Change isn't coming. It has already arrived. "It's not just looking stuff up on the internet," says Walsh. "It's going to be how we interact with all of the smart devices in our lives." Front page, welcome mat, red carpet... this is how most of us access the web. But for how much longer? Screenshot by Jackson Ryan/CNETI've been using Google Search for almost as long as it has existed. All my life, I've been driving down the information superhighway in a serviceable SUV, taking wrong turns, swerving to avoid misinformation or abuse but, ultimately, deciding where I want to end up, which roads I want to take, who I trust. I am terrified by a planet where I'm locked into a self-driving vehicle, controlled by some of the biggest tech corporations in the world, that takes me directly to my destination. The LLMs we're relying on today have proven themselves to be flawed, biased and incorrect. Trusting them to guide us is fraught with problems we're yet to fully understand. And while they may not outright replace Google Search, they're a harbinger of something even more frightening — the very real possibility of a world without it. Via Charles Tiayon
Dr. Russ Conrath's insight:
Is AI Chat a tool to assist with writing or is it a threat to copyrighted material?
Charles Tiayon's curator insight,
February 20, 2023 9:06 PM
"Within a few short years, we could find ourselves living on a planet devoid of Google Search. That might seem dramatic. After all, Google Search is probably the horse you rode in on; your first step on a microsecond-long journey across the internet that brought you to this article. Maybe you were searching for "ChatGPT" or "OpenAI" or maybe you were trying to break Google by typing "Google" into Google. (It just gives you a lot of Google, don't bother.) Maybe your smartphone served you this article because you've been reading a lot about AI at CNET lately..." #metaglossia mundus
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View a PDF of this issue Back Page Hope for change BC Centre for Disease Control Avian influenza: A BC clinician’s guide to diagnosis and management Clinical Articles Supportive cardiology: Bridging the gaps in care for late-stage heart failure patients Opioid overdose following surgery or pain treatment: A missed opportunity for intervention Council on Health Promotion Advancing health equity: The quintuple aim College Library Resources for emerging and persistent infectious diseases Editorials Are vitamins a complete waste of money? Speak-up culture = feedback culture Letters Re: Dr Ken Turnbull (obituary) Re: Gender-affirming care in BC: Guest editors reply to Drs Sinai, Regenstreif, and Leising Designation of a life insurance beneficiary News New BCMJ article types 2022 J.H. MacDermot writing prize winners Doctors of BC scholarship winners New Doctors of BC CEO Obituaries Dr Mary-Wynne Ashford (née Moar), 1939–2022 Dr Ruth Oliver, 1946–2022 Dr C. Paul Sabiston, 1954–2022 President's Comment Culture of hope Special Feature Dr Joshua Greggain: An optimistic advocate ready to engage WorkSafeBC FAQs about expedited surgeries and billing the expedited surgery premium
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Teaching writing with AI, such as GPTChat...
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