Archive for the ‘Machine Learning’ Category

Godfather of AI Says There’s a Minor Risk It’ll Eliminate Humanity – Futurism

"It's not inconceivable."Nonzero Chance

Geoffrey Hinton, a British computer scientist, is best known as the "godfather of artificial intelligence." His seminal work on neural networks broke the mold by mimicking the processes of human cognition, and went on to form the foundation of machine learning models today.

And now, in a lengthy interview with CBS News, Hinton shared his thoughts on the current state of AI, which he fashions to be in a "pivotal moment," with the advent of artificial general intelligence (AGI) looming closer than we'd think.

"Until quite recently, I thought it was going to be like 20 to 50 years before we have general purpose AI," Hinton said. "And now I think it may be 20 years or less."

AGI is the term that describes a potential AI that could exhibit human or superhuman levels of intelligence. Rather than being overtly specialized, an AGI would be capable of learning and thinking on its own to solve a vast array of problems.

For now, omens of AGI are often invoked to drum up the capabilities of current models. But regardless of the industry bluster hailing its arrival or how long it might really be before AGI dawns on us, Hinton says we should be carefully considering its consequences now which may include the minor issue of it trying to wipe out humanity.

"It's not inconceivable, that's all I'll say," Hinton told CBS.

Still, Hinton maintains that the real issue on the horizon is how AI technology that we already have AGI or not could be monopolized by power-hungry governments and corporations (see: the former non-profit and now for-profit OpenAI).

"I think it's very reasonable for people to be worrying about these issues now, even though it's not going to happen in the next year or two," Hinton said in the interview. "People should be thinking about those issues."

Luckily, by Hinton's outlook, humanity still has a little bit of breathing room before things get completely out of hand, since current publicly available models are mercifully stupid.

"We're at this transition point now where ChatGPT is this kind of idiot savant, and it also doesn't really understand about truth, " Hinton told CBS, because it's trying to reconcile the differing and opposing opinions in its training data. "It's very different from a person who tries to have a consistent worldview."

But Hinton predicts that "we're going to move towards systems that can understand different world views" which is spooky, because it inevitably means whoever is wielding the AI could use it push a worldview of their own.

"You don't want some big for-profit company deciding what's true," Hinton warned.

More on AI: AI Company With Zero Revenue Raises $150 Million

Read this article:
Godfather of AI Says There's a Minor Risk It'll Eliminate Humanity - Futurism

Prediction of ciprofloxacin resistance in hospitalized patients using machine learning | Communications Medicine – Nature.com

Smith, R. A., Mikanatha, N. M. & Read, A. F. Antibiotic resistance: A primer and call to action. Health Commun 30, 309314 (2015).

Article PubMed Google Scholar

Palumbi, S. R. Humans as the worlds greatest evolutionary force. Science 293, 17861790 (2001).

Article CAS PubMed Google Scholar

Weber, D. J. Collateral damage and what the future might hold. The need to balance prudent antibiotic utilization and stewardship with effective patient management. Int. J. Infect. Dis. 10, S17S24 (2006).

Article CAS Google Scholar

Carrara, E., Pfeffer, I., Zusman, O., Leibovici, L. & Paul, M. Determinants of inappropriate empirical antibiotic treatment: systematic review and meta-analysis. Int. J. Antimicrob. Agents 51, 548553 (2018).

Article CAS PubMed Google Scholar

World Health Organization. Executive summary: the selection and use of essential medicines 2019: report of the 22nd WHO Expert Committee on the selection and use of essential medicines: WHO Headquarters, Geneva, 1-5 April 2019. https://apps.who.int/iris/handle/10665/325773 (2019).

Chowers, M. et al. Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison. J. Antimicrob. Chemother 77, 19921995 (2022).

Article CAS PubMed Google Scholar

Nathwani, D. et al. Value of hospital antimicrobial stewardship programs [ASPs]: a systematic review. Antimicrob. Resist. Infect. Control 8, 113 (2019).

Article Google Scholar

Tribble, A. C. et al. Appropriateness of antibiotic prescribing in United States childrens hospitals: a national point prevalence survey. Clin. Infect. Dis 71, e226e234 (2020).

Article PubMed Google Scholar

eEML - Electronic Essential Medicines List. https://list.essentialmeds.org/.

Loscalzo, J. et al. Harrisons Principles of Internal Medicine, (Vol. 1 & Vol. 2). (McGraw Hill Professional, 2022).

Sharma, P. C., Jain, A., Jain, S., Pahwa, R. & Yar, M. S. Ciprofloxacin: review on developments in synthetic, analytical, and medicinal aspects. J. Enzyme Inhib. Med. Chem. 25, 577589 (2010).

Article CAS PubMed Google Scholar

Thomson, C. J. The global epidemiology of resistance to ciprofloxacin and the changing nature of antibiotic resistance: a 10 year perspective. J. Antimicrob. Chemother. 43, 3140 (1999).

Article CAS PubMed Google Scholar

Organization, W. H. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021. (2021).

Dalhoff, A. Global fluoroquinolone resistance epidemiology and implictions for clinical use. Interdiscip. Perspect. Infect. Dis. 2012, 976273 (2012).

Article PubMed PubMed Central Google Scholar

Low, M. et al. Association between urinary community-acquired fluoroquinolone-resistant Escherichia coli and neighbourhood antibiotic consumption: a population-based case-control study. Lancet Infect. Dis. 19, 419428 (2019).

Article CAS PubMed Google Scholar

Eliopoulos, G. M., Cosgrove, S. E. & Carmeli, Y. The impact of antimicrobial resistance on health and economic outcomes. Clin. Infect. Dis 36, 14331437 (2003).

Article Google Scholar

Gottesman, B. S., Carmeli, Y., Shitrit, P. & Chowers, M. Impact of quinolone restriction on resistance patterns of Escherichia coli isolated from urine by culture in a community setting. Clin. Infect. Dis. 49, 869875 (2009).

Article CAS PubMed Google Scholar

Anahtar, M. N., Yang, J. H. & Kanjilal, S. Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J. Clin. Microbiol. 59, e0126020 (2021).

Article CAS PubMed PubMed Central Google Scholar

Rawson, T. M., Ahmad, R., Toumazou, C., Georgiou, P. & Holmes, A. H. Artificial intelligence can improve decision-making in infection management. Nat. Hum. Behav. 3, 543545 (2019).

Article PubMed Google Scholar

Yelin, I. et al. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat. Med. 25, 11431152 (2019).

Article CAS PubMed PubMed Central Google Scholar

Feretzakis, G. et al. Using machine learning techniques to aid empirical antibiotic therapy decisions in the intensive care unit of a general hospital in Greece. Antibiotics 9, 50 (2020).

Article CAS PubMed PubMed Central Google Scholar

Dan, S. et al. Prediction of fluoroquinolone resistance in gram-negative bacteria causing bloodstream infections. Antimicrob. Agents Chemother. 60, 22652272 (2016).

Article CAS PubMed PubMed Central Google Scholar

Dickstein, Y., Geffen, Y., Andreassen, S., Leibovici, L. & Paul, M. Predicting antibiotic resistance in urinary tract infection patients with prior urine cultures. Antimicrob. Agents Chemother. 60, 47174721 (2016).

Article CAS PubMed PubMed Central Google Scholar

Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K. & Steyerberg, E. W. Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med. Res. Methodol. 22, 114 (2022).

Article Google Scholar

Staffa, S. J. & Zurakowski, D. Statistical development and validation of clinical prediction models. Anesthesiology 135, 396405 (2021).

Article PubMed Google Scholar

de Hond, A. A. et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. Npj Digit. Med. 5, 113 (2022).

Google Scholar

Debray, T. P. et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J. Clin. Epidemiol. 68, 279289 (2015).

Article PubMed Google Scholar

Eilers, P. H. C., Boer, J. M., van Ommen G. J. & van Houwelingen, H. C. Classification of microarray data with penalized logistic regression. in Microarrays: Optical Technologies and Informatics vol. 4266 187198 (International Society for Optics and Photonics, 2001).

Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning. vol. 1 (Springer series in statistics New York, 2001).

Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281305 (2012).

Google Scholar

Sill, J., Takcs, G., Mackey, L. & Lin, D. Feature-weighted linear stacking. ArXiv Prepr. arXiv:0911.0460 (2009).

Van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6 (2007).

Lundberg, S. M. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. in Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) 47654774 (Curran Associates, Inc., 2017).

Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565574 (2006).

Article Google Scholar

Kerr, K. F., Brown, M. D., Zhu, K. & Janes, H. Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J. Clin. Oncol. 34, 2534 (2016).

Article PubMed PubMed Central Google Scholar

Python Software Foundation. Python programming language. https://www.python.org/.

NumPy Developers. NumPy: Scientific computing with Python. https://numpy.org/doc/stable/.

Pandas Developers. Pandas: Powerful data structures for data analysis and manipulation. https://pandas.pydata.org/.

Scikit-learn developers. Scikit-learn: Machine learning in Python. https://scikit-learn.org/stable/.

XGBoost: Scalable, distributed gradient boosting. https://xgboost.readthedocs.io/en/latest/.

TensorFlow Developers. TensorFlow: An end-to-end open source machine learning platform. https://www.tensorflow.org/.

Matplotlib: A comprehensive library for static, animated, and interactive visualizations in Python. https://matplotlib.org/stable/.

SHAP Developers. SHAP: A unified approach to explain the output of any machine learning model. https://shap.readthedocs.io/en/latest/.

Gallini, A. et al. Influence of fluoroquinolone consumption in inpatients and outpatients on ciprofloxacin-resistant Escherichia coli in a university hospital. J. Antimicrob. Chemother. 65, 26502657 (2010).

Article CAS PubMed Google Scholar

Wang, T. et al. Predicting Antimicrobial Resistance in the Intensive Care Unit. ArXiv Prepr. ArXiv211103575 (2021).

Wojcik, G. et al. Understanding the complexities of antibiotic prescribing behaviour in acute hospitals: a systematic review and meta-ethnography. Arch. Public Health 79, 119 (2021).

Article Google Scholar

Diamant, M. et al. A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse. Nat. Commun. 12, 113 (2021).

Article Google Scholar

Shapley, L. S. A value for n-person games. Contrib. Theory Games 2, 307317 (1953).

Google Scholar

Kumar, I. E., Venkatasubramanian, S., Scheidegger, C. & Friedler, S. Problems with Shapley-value-based explanations as feature importance measures. in International Conference on Machine Learning 54915500 (PMLR, 2020).

Chen, M. et al. Physician and Medical Student Attitudes Toward Clinical Artificial Intelligence: A Systematic Review with Cross-Sectional Survey. Available SSRN 4128867.

Mulder, M. et al. Risk factors for resistance to ciprofloxacin in community-acquired urinary tract infections due to Escherichia coli in an elderly population. J. Antimicrob. Chemother. 72, 281289 (2016).

Article PubMed Google Scholar

Arslan, H., Azap, . K., Ergnl, . & Timurkaynak, F. On behalf of the Urinary Tract Infection Study Group Risk factors for ciprofloxacin resistance among Escherichia coli strains isolated from community-acquired urinary tract infections in Turkey. J. Antimicrob. Chemother. 56, 914918 (2005).

Article CAS PubMed Google Scholar

Beckley, A. M. & Wright, E. S. Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results. Lancet Microbe 2, e545e554 (2021).

Article CAS PubMed PubMed Central Google Scholar

Cherny, S. S., Chowers, M. & Obolski, U. Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study. medRxiv (2022).

Cherny, S. S. et al. Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling. J. Antimicrob. Chemother 76, 239248 (2021).

Article CAS PubMed Google Scholar

Lewin-Epstein, O., Baruch, S., Hadany, L., Stein, G. & Obolski, U. Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. medRxiv 2020.06.03.20120535 https://doi.org/10.1101/2020.06.03.20120535. (2020)

Chatterjee, A. et al. Quantifying drivers of antibiotic resistance in humans: a systematic review. Lancet Infect. Dis. 18, e368e378 (2018).

Article CAS PubMed Google Scholar

Truong, W. R., Hidayat, L., Bolaris, M. A., Nguyen, L. & Yamaki, J. The antibiogram: Key considerations for its development and utilization. JAC-Antimicrob. Resist. 3, dlab060 (2021).

Article PubMed PubMed Central Google Scholar

Oonsivilai, M. et al. Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a childrens hospital in Cambodia. Wellcome Open Res. 3, 131 (2018).

Article PubMed PubMed Central Google Scholar

Bell, B. G., Schellevis, F., Stobberingh, E., Goossens, H. & Pringle, M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect. Dis. 14, 125 (2014).

Article Google Scholar

Baraz, A., Chowers, M., Nevo, D. & Obolski, U. Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance. medRxiv (2022).

Fasugba, O., Gardner, A., Mitchell, B. G. & Mnatzaganian, G. Ciprofloxacin resistance in community-and hospital-acquired Escherichia coli urinary tract infections: a systematic review and meta-analysis of observational studies. BMC Infect. Dis. 15, 116 (2015).

Here is the original post:
Prediction of ciprofloxacin resistance in hospitalized patients using machine learning | Communications Medicine - Nature.com

Machine Learning Ad Agency Commits to Full Floor in NoMad – Connect CRE

Seedtag Advertising US, LLC, a leading contextual advertising company that specializes in machine learning and artificial intelligences to global brands, has signed a 5,909-square-foot lease at 13-15 W. 27thSt. The advertising company will occupy the entire third floor of the 11-story property in NoMad.The space will serve as the brands general, executive and administrative offices.

13-15 W. 27thSt. is located in a neighborhood that is currently seeing a boom in commercial activity, including new restaurants, hotels and shopping options, said Grant Greenspan, principal of the Kaufman Organization. Seedtag was attracted to the location as well as the buildings expansive floor plans and high quality, pre-built space.

Michael Heaner, Elliot Warren and Grant Greenspan of Kaufman Organization represented the landlord, 13 W 27 Leasehold LLC. Sebastian Infante and Jamie Katcher of Raise Commercial Real Estate represented the tenant. The space was previously occupied by Barstool Sports, who more than quadrupled in size and outgrew the building.

Read more from the original source:
Machine Learning Ad Agency Commits to Full Floor in NoMad - Connect CRE

Durham’s Avalo Uses Machine Learning To Let It Grow – GrepBeat

Avalo's machine-learning tech speeds the development of new crops.

Climate change is a big problem that requires big solutions, but one place where it might have an unexpected impact is on your dinner plate. By making climate-resilient crops with the help of machine-learning approach, Durhams Avalo looks to keep those plates full.

The companys Chief Scientific Officer, Mariano Alvarez, will present tomorrow (March 29) at this years Venture Connect summit in RTP.

Like many great ideas, Avalo was conceived between a pair of friends over a couple of pints. Scientist-turned-entrepreneur Brendan Collins finally convinced his friend, Duke post-doc researcher Alvarez, that his research on plant genetics could do a lot more good in the real world than in the lab.

Over a series of happy hour beers, he convinced me that it would be way more fun to start a startup and do research in that setting than to continue to apply for faculty positions, Alvarez said. So in 2020, we did just that. And he was totally rightits way more fun.

Alvarez is used to tackling plant genetics in light of climate crises. In the wake of the Deepwater Horizon oil spill crisis in 2010, he completed PhD research at the University of South Florida looking at how plants reacted to the dramatic environmental changes.

This research brought the young scientist to Duke for post-grad study on understanding the relationship between plant genomes and the environment. Guided by Duke computer science professor Cynthia Rudin, Alvarez soon realized that machine learning and computational methods could solve many of the problems in identifying genes that are meaningful to plant environmental resilience.

Around the same time that the Duke duo figured out how to use machine learning as an impactful crop-development tool, Alvarez and Collins began doing market research. The need for faster crop development was urgent, they foundand with climate changes biggest effects just decades away, the time was ripe to launch their startup.

A lot of people dont realize just how long it takes to come up with a new variety of crops, Alvarez said. You go to the store, theres different types of tomatoes, theres different types of cucumbers, and you sort of imagine that theyre all just sitting around. It actually takes a long time for somebody to develop those varieties, anywhere between seven and 15 years. Its roughly a $200 million process to actually get them through trials and into farmers fields.

Collins, who is Avalos CEO as well as co-founder, brought his software-scaling skills from previous startup ventures, allowing them to translate this computational model into a marketable product.

Using its computational model, Avalo can rapidly test for genes that may produce a desired phenotypic outcome in a plant. The companys computational engine allows them to discover the genetic basis of complex traits, even from patchy data.

This not only makes the process of developing new crops much faster, it also makes it cheaper by slashing the number of years needed for research and development.

Traditionally, crop development has focused on traits that will make a process that takes 15 years and $200 million worth their while, usually aiming for genetic variations that lead to high yield or herbicide resistance. With Avalos technology, companies can focus on other traits, like ones that make a crop able to grow effectively in the new temperatures that result from climate change, or even tweaking a crop for better taste. This technology is perfect for a diverse industry with diverse needs.

One thing that was really interesting going into agriculture is just the scope of all of the things that people are looking for, and how diverse the agricultural system is, Alvarez said. And how unique growers needs are.

Avalo offers three buckets of product, Alvarez says. In the computational bucket falls Avalos work in providing their computational tools to companies who know what traits they want but dont have the technology to make it happen. In the second bucket, Avalo transfers specific traits into a plant for a customer.

In the third bucket is Avalos front-to-back operations. The company is currently working on a heat-tolerant variety of Chinese broccoli, for example, but their capabilities are not crop-limited, especially with the help of their three greenhouses spread across the Triangle.

The company is growing fast, but then again, so is the problem it looks to addressclimate change waits for no man, or machine.

We really only have about 30 more years until some of the biggest changes start to take effect, Alvarez said. If development takes 15 years, then we only have two shots, which is just not enough time to develop the varieties that we think were going to need to to adapt an entire agricultural system to a whole new climate.

See original here:
Durham's Avalo Uses Machine Learning To Let It Grow - GrepBeat

Collaborative machine learning startup FedML raises $6M to train … – SiliconANGLE News

Collaborative artificial intelligence startup FedML Inc. said today it has closed on a $6 million seed funding round that will help it bring together companies and developers to train, deploy and customize machine learning models anywhere, across thousands of edge- and cloud-hosted nodes.

Todays round was led by Camford Capital and saw participation from Plug and Play Ventures, AimTop Ventures, Acequia Capital and LDV Partners.

Despite only just closing on its first round of funding, FedML has already created an open-source community, enterprise platform and various software tools that make it easier for people to collaborate on machine learning projects. They can do this by sharing data, models and compute resources, the company explained.

FedMLs mission is to create an ecosystem that will meet enterprise demands for custom AI models. It says that there are a number of businesses that want to train or fine-tune AI models on their own data so they can leverage them for more specific tasks such as business automation, customer service, product design and so on. But this data is often extremely sensitive and regulated, or else siloed, making it difficult to use cloud-based AI training systems.

To overcome this, FedML has created a federated learning platform that makes it possible for developers to collaboratively train AI models using private or siloed data at the edge, without needing to move that data anywhere else. FedML calls this approach learning without sharing. So, for example, a retail company could build models for personalized shopping recommendations without exposing a customers private data. A healthcare company would be able to build an AI model thats able to detect rare diseases by training it on scarce and extremely sensitive healthcare records that might be spread across multiple hospitals.

According to FedML co-founder and Chief Executive Salman Avestimehr, the future application of AI will depend on these kinds of collaborations. We want to create a community that trains, serves and mines the best AI models, he said. For example, we enable data owners to contribute their data to a machine learning task, and they can work with AI developers or training specialists to build a customized machine learning model, and everyone gets rewarded for their contributions.

Besides bringing the concept of federated learning to AI, FedML believes its collaborative approach will help to overcome the cost and complexity of large-scale AI development. OpenAI LP, the company that built ChatGPT, spent millions of dollars to train that model.

Of course, many companies do not have that kind of money to throw at AI training, meaning that the best models are limited to only the biggest technology firms. AI training is not only expensive, but also very complex, requiring significant expertise that not every company has. FedML reckons these challenges can be overcome with its collaborative, open-source AI development community.

We allow people to train anywhere and serve anywhere, from edge to cloud, enabling lower-cost and decentralized AI development thats accessible to everyone, said FedMLs other co-founder and Chief Technology Officer Chaoyang He.

FedMLs platform was launched in March 2022 after three years of development, and has already surpassed Google LLCs TensorFlow Federated as the most popular open-source library for federated machine learning projects. In addition, the company has created an MLOps ecosystem for training machine learning models anywhere at the edge or in the cloud. This ecosystem has more than 1,900 users, who have deployed FedML more than 3,500 edge devices and trained more than 6,500 models.

The startup has also signed 10 enterprise contracts spanning industries such as healthcare, financial services, retail, logistics, smart cities, Web3 and generative AI.

Constellation Research Inc. Vice President and Principal Analyst Andy Thuraitold SiliconANGLE that FedML has gained quite a bit of traction since its release last year, thanks to its open-source libraries and its cheaper pricing model. However, he said it has barely made a dent in terms of the full machine learning operations lifecycle. More and more, enterprises are looking towards full cycle MLOps platforms because its difficult to bring best-of-breed ML models to market without them, he explained.

That said, Thurai thinks FedML offers a lot of potential, especially if the concept of training smaller models using private datasets takes off. He said FedMLs advantage is it enables model training without needing to share data, which can be extremely useful in regulated industries and regions where data privacy is of special importance, such as the EU.

If the concept of model training at the edge using localized data takes off, then FedML can have a big impact on this, Thurai said. For now, LLMs and ChatGPT-type models are the craze, with most enterprises going for bigger and better AI models, so it will take some time to change that mindset.

Though a lot of work remains to be done, Camford Capitals partner Ali Farahanchi said he was impressed with FedMLs compelling vision and unique technology, which will enable open and collaborative AI at scale. In a world where every company needs to harness AI, we believe FedML will power both company and community innovation that democratizes AI adoption, he said.

Go here to see the original:
Collaborative machine learning startup FedML raises $6M to train ... - SiliconANGLE News