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Artificial intelligence (AI) vs. machine learning. Just the words can bring up visions of decision-making computers that are replacing whole departments and divisionsa future many companies believe is too far away to warrant investment. But the reality is, AI is here, and here to stay. And particularly at the enterprise level, a growing number of companies are tuning in to the productivity and promise of machines that can think for themselves.

In fact, a recent study by McKinsey showed that by 2019, venture capital investment in AI had already topped $18.5 billion. And IDC predicted that by 2023, global spending on AI and Machine Learning solutions will reach nearly $98 billion.

All this development promises to have a tremendous impact on every corner of industry. McKinsey recently released figures predicting that by 2030, 375 million workersabout 14 percent of the total global workforcewill need to switch occupations as robots and algorithms take over tasks once done by humans. Yet most analyses project net job gains as a result of AIlike this report from Gartner, which predicts that in the US, AI will displace as many as 1.8 million jobs in the near future, yet experience a net gain of at least 500,000 to two million new jobs as companies expand to absorb the new productivity.

So, with all that in mind, how do you understand dial back the AI vs. machine learning hype? And how should you be thinking about what cognitive computing can do for your business? Lets take a closer look.

Artificial intelligence is a computer system designed to think the way humans think. That means more than just doing one task well, like say, Alexa, who responds to your voice command to play your favorite song. True artificial intelligence has the ability to parse data, make decisions, and learn from those decisions to create something new.

AI has been famously used to tackle big problems, like testing drug compounds for curing cancer. Alibaba uses AI not just for predictive advertising on their sites, but also for monitoring cars and creating constantly changing traffic patterns, or helping farmers monitor crops to increase yield. Amazon Go is using AI to rethink the future of retail, creating unmanned convenient stores that monitor your shopping experience and charge you automatically when you walk out the door with an item.

Experimental AI has written novels (badly), played chess against world masters (very well), and parsed the worlds medical literature to help doctors make better and more complete diagnoses (and saved lives.) With AI platforms like Microsoft Azure, Google Cloud, and many others, developers now have the resources they need to think creatively about AI for their own businesses. Further, AI in the cloud significantly reduces a companys infrastructure costs for the massive computing capacity AI needs to be most useful.

Sometimes, machine learning is used interchangeably with artificial intelligence, but thats not quite correct. Machine learning is actually a subset of artificial intelligence. Machine learning refers to a program which does one task really well by parsing and analyzing data over time. It is only as good as the data flowing into it. However, examples of machine learning are all around us, from Alexa on our tabletops, to the dynamic pricing that goes up or down on a website based on your personal information, or the email that gets automatically filtered to your inbox, and the chatbot that responds when you ask a question on a website.

Artificial Intelligence has promise, and is becoming more feasible for companies to incorporate into their systems, says Sitima Fowler, vice president of marketing for national IT consulting firm Iconic IT. But she recommends most companies start small.

AI is trendy right now, definitely. But the reality is, most companies will be starting with machine learning, such as bots that parse their user traffic, for instance, to mine data. They might use it for chatbots on their website to direct consumer inquiries to the right information. From there, many companies can use the AI development tools available in the cloud from services like Amazon and Microsoft to develop AI that powers their consumer facing apps, and so much more. Were all very excited about the future of where artificial intelligence can take us. But its important to take it one step at a time, so the rest of your systems can integrate and keep up, Fowler said.

For example, at Iconic IT, we use AI to prevent cyber security breaches.Just simply installing an antivirus and email spam filter on your computer isnt enough. The bad guys have figured out ways around this software.So we incorporate AI on top of these software so it looks as the persons normal behavior and interactions with other people. Over time it learns a users email habits, communication styles, contacts to determine if a particular email is legitimate or potentially harmful, she added.

Originally posted here:
AI vs. Machine Learning: Their Differences and Impacts - CIO Insight

In the field of drug discovery, one must always begin with the target, right? Not if you ask Cellarity, a quickly emerging biotech company revolutionizing the drug development space.

Rather than the traditional target centric approach to drug discovery, Cellarity works at the level of the cell to understand how disease impacts cell behavior via a target agnostic approach that can help illuminate the most complex diseases science has not yet been able to crack.

For decades, drug discovery has been about reducing diseases down to a single molecular target that we can drug to influence the course of a given disease, explained Cellarity CEO Fabrice Chouraqui, who is also a CEO-Partner at Flagship Pioneering. This approach has produced a significant number of breakthrough treatments, but the target-centric assumptions that we make in vitro or in vivo do not often translate into human. Human biology is far more complex than any single target could ever predict, which is one reason why right now many drugs fail in clinical development. Our approach is different.

Founded in 2019 and already rising to the top of lists such as BioSpaces own Top Life Sciences Startups to Watch in 2021, Cellarity believes there is a better wayone based on the computational modeling of cell behavior.

Cellaritys unique platform generates unprecedented biological insights by combining unique expertise in network biology, high-resolution data, and machine learning. The result is a new understanding of the cells trajectory from health to disease and how cells relate to one another in tissues. This in turn opens up a world of opportunities for the discovery of novel therapeutics, particularly for complex diseases.

Diseases are complex and often not linked to a single target in a single cell in a single system, said Chouraqui. Conditions like T cell exhaustion, metabolic disease, complex neurodegenerative diseases like Alzheimerstheres a reason we havent made a lot of progress in these areas. So we asked ourselves if there was a way to work at a higher levelthe level of the cellto really harness the complexity of human biology.

Because Cellaritys pioneering approach does not start with a single molecular target, its scientists are able to uncover a much more diverse set of compounds that can be deeply characterized to understand how they work on both known and previously unknown targets. Indeed, the companys algorithms, data assets and approach were inspired by systems biology.

In the early 2000s, systems biology proposed that by looking at biology as a whole, we would be able to better understand the interplay between its parts, specifically genes, proteins and pathways in the context of disease and health, said Cellarity Chief Digital and Data Officer Milind Kamkolkar. However, due to a lack of well-integrated high-resolution data and sophisticated computational power, the industry had no choice but to study biologys parts in the absence of its networks.

A lot has changed since then. In the past five years alone, phenotypic drug discovery has evolved as an alternative to the single-target approach, thanks to advances in high-throughput imaging technology and machine learning. Yet the gap between a drugs success in vitro and an efficacious drug in patients remains immense.

Cellaritys solution: Unlike single molecules, single target or phenotypic representations of cellular programs, Cellarity directly targets cellular programs critical to disease, leveraging a platform that systematically addresses the problems of translation beyond simplifying target discovery, toxicity, adverse effects, and drug design.

One key part of the approach is the way Cellarity predicts drugs and their properties by tying them to computationally engineered representations of cell behavior called Cellarity Maps.

Cellarity Maps give us a much higher-resolution picture of the cellular components of a tissue and really allow us to understand the mechanism of action that one would want to reverse to go from a state of disease to a state of health, said Chouraqui.

Chouraqui believes that there is no limit to where this cell-centric approach can take Cellarity and the field of medicine. His assertion is backed up by the cadre of investors that recently put up $123 million in series B financing.

Our investors recognized that Cellarity stood out in the field of drug discovery, said Saif Rathore, MD PhD, Cellaritys VP and Head of Strategy and Partnerships. We are the only company taking a target-agnostic approach that evaluates cell behavior changes and works through product optimization, whereas others in the field are primarily working on optimizing different parts of the target centric molecular or phenotypic drug discovery processes.

To execute its vision, Cellarity has assembled a team of diverse, world class talent. We have brought together international leaders from pharma, graduates of Flagship Pioneering academic programs, physicians, scientists, and pedigrees that span the spectrum from the Broad Institute to McKinsey. said Rathore.

The outcome: the pioneering biotech already has 7 drug discovery programs underway across 10 therapeutic areas including the high-value fields of hematology, immuno-oncology, metabolism, and respiratory.

All diseases stem from a disorder at the cellular level, said Chouraqui. This cell-centric approach can be applied to virtually every single disease. We are progressing programs in diverse therapeutic areas to show the depth of our platform, starting with diseases for which there is a well-understood and direct correlation between a change in cell behavior and the etiology of the disease.

Chouraquis vision for the platform transcends the company. In a few years time, he sees Cellarity with unparalleled predictive power in different drug modalities and a deep exploratory pipeline with multiple clinical proofs of concept in different disease areas.

Our platform has the potential to change how the world approaches the discovery of medicines said Chouraqui.

Featured Jobs on BioSpace

Continued here:
Cellarity: Transforming Drug Development at the Confluence of Biology and Machine Learning - BioSpace

This is an excellent overview at Stat on the current problems with machine learning in healthcare. Its a very hot topic indeed, and has been for some time. There has especially been a flood of manuscripts during the pandemic, applying ML/AI techniques to all sorts of coronavirus-related issues. Some of these have been pretty far-fetched, but others are working in areas that everyone agrees that machine learning can be truly useful, such as image analysis.

How about coronavirus pathology as revealed in lung X-ray data? This new paper (open access) reviewed hundreds of such reports and focused in on 62 papers and preprints on this exact topic. On closer inspection, none of these is of any clinical use at all. Every single one of the studies falls into clear methodological errors that invalidate their conclusions. These range from failures to reveal key details about the training and experimental data sets, to not performing robustness or sensitivity analyses of their models, not performing any external validation work, not showing any confidence intervals around the final results (or not revealing the statistical methods used to compute any such), and many more.

A very common problem was the (unacknowledged) risk of bias right up front. Many of these papers relied on public collections of radiological data, but these have not been checked to see if the scans marked as COVID-19 positive patients really were (or if the ones marked negative were as well). It also needs to be noted that many of these collections are very light on actual COVID scans compared to the whole database, which is not a good foundation to work from, either, even if everything actually is labeled correctly by some miracle. Some papers used the entire dataset in such cases, while others excluded images using criteria that were not revealed, which is naturally a further source of unexamined bias.

In all AI/ML approaches, data quality is absolutely critical. Garbage in, garbage out is turbocharged to an amazing degree under these conditions, and you have to be really, really sure about what youre shoveling into the hopper. We took all the images from this public database that anyone can contribute to and took everyones word for it is, sadly, insufficient. For example, one commonly used pneumonia dataset turns out to be a pediatric collection of patients between one and five, so comparing that to adults with coronavirus infections is problematic, to say the least. Youre far more likely to train the model to recognize children versus adults.

That point is addressed in this recent preprint, which shows how such radiology analysis systems are vulnerable to this kind of short-cutting. Thats a problem for machine learning in general, of course: if your data include some actually-useless-but-highly-correlated factor for the system to build a model around, it will do so cheerfully. Why wouldnt it? Our own brains pull stunts like that if we dont keep a close eye on them. That paper shows that ML methods too often pick up on markings around the edges of the actual CT and X-ray images if the control set came from one source or type of machine and the disease set came from another, just to pick one example.

To return to the original Nature paper, remember, all this trouble is after the authors had eliminated (literally) hundreds of other reports on the topic, for insufficient documentation. They couldnt even get far enough to see if something had gone wrong, or how, because these other papers did not provide details of how the imaging data were pre-processed, how the training of the model was accomplished, how the model was validated, or how the final best model was selected at all. These fall into Paulis category of not even false. A machine learning paper that does not go into such details is, for all real-world purposes, useless. Unless you count putting a publication on the CV as a real-world purpose, and I suppose it is.

But if we want to use these systems for some slightly more exalted purposes, we have to engage in a lot more tire-kicking than most current papers do. I have a not-very-controversial prediction: in coming years, virtually all of the work thats being published now on such systems is going to be deliberately ignored and forgotten about, because its of such low quality. Hundreds, thousands of papers are going to be shoved over into the digital scrap heap, where they most certainly belong, because they never should have been published in the state that theyre in. Who exactly does all this activity benefit, other than the CV-padders and the scientific publishers?

Read the original:
Machine Learning Deserves Better Than This | In the Pipeline - Science Magazine

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Machine Learning as a Service (MLaaS) Market to Witness Huge Growth by 2028 | Microsoft, International Business Machine, Amazon Web Services KSU |...