AI News, Healthcare Costs Continue To Soar As Patients Look For A New ... artificial intelligence
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.
Colloquially, the term 'artificial intelligence' is often used to describe machines (or computers) that mimic 'cognitive' functions that humans associate with the human mind, such as 'learning' and 'problem solving'.
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.
Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.
This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding;
and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as '0' and '1', could simulate any conceivable act of mathematical deduction.
The success was due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.
According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a 'sporadic usage' in 2012 to more than 2,700 projects.
He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.
Computer science defines AI research as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
A more elaborate definition characterizes AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.”
An AI's intended utility function (or goal) can be simple ('1 if the AI wins a game of Go, 0 otherwise') or complex ('Do mathematically similar actions to the ones succeeded in the past').
Alternatively, an evolutionary system can induce goals by using a 'fitness function' to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food.
Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data.
Some of the 'learners' described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world.
In practice, it is almost never possible to consider every possibility, because of the phenomenon of 'combinatorial explosion', where the amount of time needed to solve a problem grows exponentially.
The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: 'After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza'.
A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial 'neurons' that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to 'reinforce' connections that seemed to be useful.
Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.
Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.
A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.
instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects.
Humans also have a powerful mechanism of 'folk psychology' that helps them to interpret natural-language sentences such as 'The city councilmen refused the demonstrators a permit because they advocated violence'.
For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.
By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.
These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a 'combinatorial explosion': they became exponentially slower as the problems grew larger.
In addition, some projects attempt to gather the 'commonsense knowledge' known to the average person into a database containing extensive knowledge about the world.
by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern).
They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or 'value') of available choices.
A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts.
Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well.
Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications.
is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world.
a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its 'object model' to assess that fifty-meter pedestrians do not exist.
Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.
the paradox is named after Hans Moravec, who stated in 1988 that 'it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility'.
Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.
Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.
These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI.
Many researchers predict that such 'narrow AI' work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.
One high-profile example is that DeepMind in the 2010s developed a 'generalized artificial intelligence' that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.
hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to 'slurp up' a comprehensive knowledge base from the entire unstructured Web.
Finally, a few 'emergent' approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.
For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence).
When access to digital computers became possible in the mid 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation.
in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science.
Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems.
Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.
His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.
found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior.
By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition.
This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).
Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.
Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results.
However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures.
Compared with GOFAI, new 'statistical learning' techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets.
The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models;
In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.
Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.
In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called 'pruning the search tree').
These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top.
For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses).
Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).
Fuzzy set theory assigns a 'degree of truth' (between 0 and 1) to vague statements such as 'Alice is old' (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false.
Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as 'if you are close to the destination station and moving fast, increase the train's brake pressure';
Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).
Complicated graphs with diamonds or other 'loops' (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities.
Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise.
Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as 'naive Bayes' on most practical data sets.
A simple 'neuron' N accepts input from multiple other neurons, each of which, when activated (or 'fired'), cast a weighted 'vote' for or against whether neuron N should itself activate.
one simple algorithm (dubbed 'fire together, wire together') is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another.
In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending;
The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events).
Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ('fire together, wire together'), GMDH or competitive learning.
However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches.
For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a 'credit assignment path' (CAP) depth of seven.
Deep learning has transformed many important subfields of artificial intelligence[why?], including computer vision, speech recognition, natural language processing and others.
In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning.
Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.
In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.
Researcher Andrew Ng has suggested, as a 'highly imperfect rule of thumb', that 'almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.'
The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.
The 'imitation game' (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.
High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,
With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution,
In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.
Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.
One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.
The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.
However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.
Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.
Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.
For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing.
Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.
Intelligence technologies enables coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).
It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.
Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.
He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down.
If this AI's goals do not reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal.
Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants.
It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.
Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.
The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.
The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: 'Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines.
In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines.
Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence.
Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share).
I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence.'
The philosophical position that John Searle has named 'strong AI' states: 'The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.'
Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization.
Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.
A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed.
In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later 'the Gynoids' book followed that was used by or influenced movie makers including George Lucas and other creatives.
Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.
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IN 2009, Weberg defined innovation in health care as “something new, or perceived new, by the population experiencing the innovation, that has the potential to drive change and redefine healthcare's economic and/or social potential.”1(p235) Most innovations hailing from Weberg's context are sustaining innovations, which are innovations that improve existing products among an established consumer base.
For example, increasing the number of razor blades in a commercial razor from 4 to 5 blades or improving the resolution quality of a flat-screen TV is an example of sustaining innovations.2 Larger incumbents in the market typically produce these or generally represent advances in engineering, technology, and manufacturing to make goods, products, or services better.2 Disruptive innovation, on the contrary, is defined as an improvement that originates in low-end or new-market footholds and pushes incumbents closer to the top of the market or out of the market altogether.2,3 Nurses, as vital participants in leading change that improves outcomes for patients, are essential partners in conception and implementation of both types of innovation.
Instead, Uber launched in the mainstream market of San Francisco and offered a sustaining innovation to the existing taxi market and created a seemingly better service with easier access, cleaner vehicles, and more relevant payment methods.2 Uber did not enter the low-end and underserved markets until after it had established its business in the mainstream.
Traditional visits to a primary care clinic are often wrought with friction-inducing barriers such as scheduling, commutes, and long wait times for a condensed provider visit that carries a high price tag.4 Now, health care consumers can visit commercial giants such as Walgreens, CVS, and Walmart where they will experience primary care in a very different way.
Health care continues to be dominated by high costs, variable access, questionable quality, and inconsistent outcomes across the country.5 Even with advancements in electronic medical record technology, high-end imaging, and more sophisticated monitoring equipment, most health systems exhibit slow adoption of technology along with an aversion to disruptive business models.
Many early products created by disruptive innovators lack the quality, validation, and scalability that health care purchasers demand in a highly regulated and somewhat archaic business model.5 In health care, sustaining innovations are much safer plays from a purchasing and user adoption standpoint.
Nurse leaders are often positioned as conflict managers in these competing systems and may more aptly be utilized as innovation leaders.8 In the lens of disruptive innovation, nurse leaders have keen insight and ability to “connect the dots” between incidents that appear to be unrelated.6 The task at hand may appear overwhelming, considering that health care is ripe for disruption.
Wages for nurses continue to rise nationally, and health system operational budgets are significantly influenced by the dominant direct and indirect nursing labor costs.9,10 Health care employers strive to be smarter, more agile, and more informed about hiring decisions and talent management operations.
Some vendors have created technology that can match employees with benefits packages that suit their individual needs, much like Netflix or Amazon matches consumers with products based on prior behaviors.11 Advancements in big data and analytics technology allow employers to glean greater insights into potential hires.
The cognitive, emotional, and behavioral learning dimensions exhibited with extended reality technologies (such as VR, augmented reality, and cross-reality) have been shown to improve training experiences for users over traditional methods.14 A recent study at the University of Maryland found higher recall and spatial awareness with head-mounted VR displays than desktop-based learning.15 For high-risk areas such as operating rooms, VR simulation has demonstrated improved performance in a risk-free learning environment.
A study published in 2017 found a 250% learner improvement using VR over traditional reading and lecture training when preparing the perioperative staff for the management of surgical fires.16 Available at a fraction of the cost of high-fidelity simulation manikins, VR training possesses all the traits of a disruptive innovation: low-end price entry, fringe customer engagement to the higher end of the market, scalability to the masses, and a new market creation in health care.
The high-stakes emphasis on NCLEX-RN pass rates has essentially forced academic institutions to concentrate on examination pass rates more than the overall competency of new graduate nurses.17 It has also limited innovation and disruptions by mandating that all prelicensure students gain knowledge in multiple nursing specialties prior to graduation (including obstetrics, pediatrics, mental health, and adult care).
Ultimately, the result is that graduates applying to health care systems are underprepared to transition into employment in any specialty without expensive and time-consuming training programs.18–21 When this is coupled with the retirement trend of experienced nurses, current and future shortages of nurses in specialty areas (including perioperative, behavioral health, ambulatory, and perinatal nursing) are likely to increase.19,20,22,23 Nurse leaders from both academia and practice must collaborate as strategic advisors on disruptions that aim to improve initial graduate clinical competency as well as workforce readiness to fill gaps in specialty nursing practice.
Curriculum redesign in these institutions has addressed the need for additional education in community-based and primary care nursing.24 For example, the Jefferson College of Nursing has redesigned its curriculum to a concept-based curriculum where prelicensure students are now exposed to a broader range of clinical settings, including acute care, continuing care, outpatient rehabilitation, pediatric offices, homeless shelters, and accountable care organizations.25 Jefferson has accomplished this while still preparing student nurses to successfully pass the NCLEX RN examination.25 Another example of disruption is collaborative academic-practice partnerships that provide externships and clinical immersion experiences in areas with inadequate prelicensure exposure such as perioperative services.23 Emulating a version of the medical education model, which leverages academically driven residencies that continue formal education post–initial licensure, should also be considered as a disruptive model for nursing.
In a cycle described as the “disruptor's gambit,” the disruptor reveals its intentions early on and follows rapid adaptation cycles to respond to customers and partners while leveraging them as force multipliers in the new ecosystem.27 Researchers have pointed out that various actors inside an emerging ecosystem can play important roles in legitimizing the new business models forming around them.27 In the case of health care disruption, nurse leaders occupy a strategic role in the disruptor's gambit by actively building frames for the next adaptation cycle.
Without nurse leader support, disruptive innovation in health care will degenerate into a vicious cycle of stagnation and incumbent-constraint.27 Given the size, scale, and dissemination of the global nursing workforce, nurse leaders have the necessary influence to function as force multipliers.
Understanding that innovation is often a social phenomenon, nurse leaders are positioned to broker innovation within highly connected groups, such as nurses, by using social capital, group cohesion, and cultural influence to drive adaptation cycles and rapid advancement of a disruptive model.8 Every day, nurse leaders navigate the health care conundrum of balancing cost, quality, and resources while relentlessly focusing on positive experiences for all stakeholders.
Pharma’s AlphaGo Moment: For the First Time, Artificial Intelligence Has Designed a New Drug in 21 Days - Deep Knowledge Analytics
“This is the first time that an AI company has designed a novel drug from scratch, synthesized it and preclinically validated it end-to-end in days rather than years –
The data-driven approach will give better and faster results than the traditional methods, leading to faster drug discovery and safer, more reliable results than clinical trials on their own.
Insilico Medicine starts with molecular leads that have been specifically designed, in terms of their pharmacokinetic and pharmacodynamic properties, and therefore have a higher probability of being effective for specific disease targets.
“This newest achievement made by Insilico Medicine, a leading AI for drug discovery and longevity company and an official partner of Ageing Research at King’s, demonstrates the truly disruptive potential that AI holds in terms of accelerating the pace of progress in drug discovery.
It is also quite notable that the team released the code behind their algorithm in an open-source format, allowing other researchers to apply their techniques and build upon their achievements for the advancement of the entire field of AI for drug design, ageing research and longevity” said Richard Siow, Ph.D., Director of Ageing Research at King’s and former Vice-Dean (International), Faculty of Life Sciences &
Another paper, published in Molecular Pharmaceutics in 2016, demonstrated the proof of concept of the application of deep neural networks for predicting the therapeutic class of the molecule using the transcriptional response data.
For example, deep learning and GANs (Generative adversarial networks) are providing opportunities for reducing the timeline for molecule hit discovery in a matter of weeks when compared to years with the traditional approach.
Sullivan created a new award category for this specific area of research and development is indicative of the interest and support that AI for drug discovery is garnering from the drug development community.
WuXi AppTec is committed to enabling innovative collaborators to bring innovative healthcare products to patients, and to fulfilling WuXi’s dream that every drug can be made and every disease can be treated. “As far as I know, this marks the first ever demonstration that AI can generate entirely novel, synthesizable, active molecules against a specific pharmacological target.
Since then they have been the first to use cutting edge deep learning techniques like Generative Adversarial Networks to design novel drug candidates from scratch with specified molecular properties in 2016, and in 2018 to succeed in designing, synthesizing and validating a new drug end to end in less than 2 months.
Pharma Division of Deep Knowledge Analytics is the leading analytical agency specifically focused on deep intelligence of the pharma industry and the AI for Drug Discovery sector, and a specialized department of Deep Knowledge Analytics, a DeepTech-focused analytical company focusing on advanced industry analytics on the topics of Artificial Intelligence, GovTech, Blockchain, FinTech, Invest-Tech and Frontier Technologies.
Knowledge Ventures is a leading investment fund focused on the synergetic convergence of DeepTech, frontier technologies and technological megatrends, known for its use of sophisticated analytical system for investment target identification and due-diligence.
Deep Knowledge Ventures led Insilico Medicine’s seed funding round in 2014, and has remained a close advisor in the company’s journey towards becoming a global leader in the application of advanced AI for aging research and the extension of healthy human longevity.
DKV’s AI-Pharma Specialized Fund combines the profitability of venture funds with the liquidity of hedge funds significantly de-risking the interests of LP’s and simultaneously providing the best and most promising AI companies with a relevant amount of investment.
- On 26. februar 2021
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