AI & Machine Learning News. 24, February 2020
The Artificial Intelligence and Machine Learning News clippings for Quants are provided algorithmically with CloudQuant’s NLP engine which seeks out articles relevant to our community and ranks them by our proprietary interest score. After all, shouldn’t you expect to see the news generated using AI?
Elon Musk and Talosian Jeff Bezos in Star Trek’s pilot episode ‘The Cage’ deepfake.
AI is Changing the Pattern for How Software is Developed
Software developers are using AI to help write and review code, detect bugs, test software and optimize development projects. This assistance is helping companies to deploy new software more efficiently, and to allow a new generation of developers to learn to code more easily.
These are conclusions of a recent report on AI in software development published by Deloitte and summarized in a recent article in Forbes. Authors David Schatsky and Sourabh Bumb describe how a range of companies have launched dozens of AI-driven software development tools over the past 18 months. The market is growing with startups raising $704 million in the year ending September 2019.
The new tools can be used to help reduce keystrokes, detect bugs as software is being written and automate many of the tests needed to confirm the quality of software. This is important in an era of increasing reliance on open source code, which can come with bugs. While some fear automation may take jobs away from coders, the Deloitte authors see it as unlikely. “For the most part, these AI tools are helping and augmenting humans, not replacing them,” Schatsky stated. “These tools are helping to democratize coding and software development, allowing individuals not necessarily trained in coding to fill talent gaps and learn new skills. There is also AI-driven code review, providing quality assurance before you even run the code.”
2020-02-21 12:37:45+00:00 Read the full story…
Weighted Interest Score: 3.8938, Raw Interest Score: 1.9255,
Positive Sentiment: 0.1590, Negative Sentiment 0.1943
CloudQuant Thoughts : I use KITE in PyCharm which I find very useful. Just having an AI buddy next to you saying “I think you meant to put a double equals there!” is an astonishing saving in time. If you do a lot of programming, particularly in Python, you should check out AI assistance.
Python Dominates, Usage Survey Confirms
Data scientists, machine learning developers and data engineers are turning decisively to the Python programming language, according to a new study.
An annual usage analysis released this week by O’Reilly Media also found a decided shift towards cloud native design for software, IT infrastructure and DevOps. The study tracked the most popular search terms on O’Reilly’s platform in 2019. The fastest growing search terms were “coding practices,” which jumped nearly 40 percent year-on-year.
Another hot topic as data and applications shift to the cloud was security. A pair of security certifications developed by the industry group CompTIA spiked over the past year, reflecting the need for more security skills as companies move to the cloud. Overall, security registered the strongest growth as a topic search in 2019, jumping nearly 30 percent.
Meanwhile, Python’s growing popularity was fueled by machine learning development. The survey found that Python usage for AI, deep learning and natural language processing projects grew by 9 percent over 2018. Java ranked second in 2019, but usage actually declined slightly year-on-year.
2020-02-19 00:00:00 Read the full story…
Weighted Interest Score: 3.8080, Raw Interest Score: 2.2084,
Positive Sentiment: 0.3118, Negative Sentiment 0.0520
CloudQuant Thoughts : No surprise here at CloudQuant, we obviously use Python extensively both in our scripting language for ANYONE to develop auto-trading models and for all of our back end processes. Everyone in the firm understands Python, not just our programmers. It makes for a very fertile environment.
Autonomous cars ‘won’t kill insurance’
“It might not be how the car and another vehicle interact, but it might be about how the car interacts with the environment or the road system that it’s driving on.”
About 60 per cent of IAG’s overall revenue comes from motor vehicle insurance premiums, and Ms Batch said IAG spent considerable time preparing for an increasingly AI-dense world across its business lines.
This goes far beyond driverless cars to incorporate preparations for big str…
2020-02-17 00:00:00 Read the full story…
Weighted Interest Score: 3.5977, Raw Interest Score: 1.4196,
Positive Sentiment: 0.1052, Negative Sentiment 0.1840
CloudQuant Thoughts : Wanna bet?
Green Bond Indices And ESG Futures Outperform
Green bond indices have, in general, performed better than traditional indices over the past four years according to NN Investment Partners, the Dutch fund manager.
Bram Bos, lead portfolio manager green bonds at NN IP, said in a statement that investing in green bonds is an easy way to invest in fixed income more sustainably without having to compromise on performance.
“Green bonds are typically issued by innovative, forward-looking issuers, whose activities are adapting to the urgency of climate change. As a result, these companies are less exposed to climate and environmental, social and governance risks and are more transparent in their activities,” added Bos. “The consistent outperformance of green bond indices versus regular bond indices underscores this and also makes a compelling argument for green bonds in a broader context.”
2020-02-21 14:10:12+00:00 Read the full story…
Weighted Interest Score: 3.2173, Raw Interest Score: 1.7629,
Positive Sentiment: 0.2063, Negative Sentiment 0.2626
CloudQuant Thoughts : We are strong proponents of ESG, one of the leading datasets on our Alternative Data Catalog is an ESG dataset from G&S Quotient. Though there has been some concern recently that some ESG Indices and Funds are promoting themselves as ESG and loading up on the FAANG stocks. Check out this mornings “DAILY” Podcast from The New York Times where they discuss last weeks letter from Larry Fink where he states “I believe we are on the edge of a fundamental reshaping of finance.“. Jeff Bezos’ Amazon Monday promised to donate $10 billion dollars “to explore new ways of fighting the devastating impact of climate change on this planet we all share” including changing all their delivery trucks to Electric. Delta have promised to go Carbon Neutral within 10 years. Microsoft followed up with a $10b 10 year commitment to climate change, promising to go Carbon Negative. Of course a lot of these extreme promises are based on future technology such as Carbon sequester/capture. However without the buy in from the current major polluters of China and India its impact will be negligible.
The Ultimate Beginner Guide to TensorFlow
Why TensorFlow? We already have Keras! When we build a machine learning model, for example, a convolutional neural network for classifying images, we usually design our network on top of high-level libraries such as Keras. At the end of the day, a Keras model is converted into a TensorFlow program.
TensorFlow, open sourced to the public by Google in 2015, is the result of years of lessons learned from a dilemma: should we attempt to do research with inflexible libraries so that we don’t have to reimplement code, or should we use one library for research and a completely different library for production? TensorFlow was made to be flexible, efficient, extensible, and portable (source). Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. TensorFlow embraces both open source communities and stability of a large corporation.
2020-02-24 05:07:40.742000+00:00 Read the full story…
Weighted Interest Score: 5.3957, Raw Interest Score: 2.2490,
Positive Sentiment: 0.1719, Negative Sentiment 0.1003
10 Datasets For Data Cleaning Practice For Beginners
In order to create quality data analytics solutions, it is very crucial to wrangle the data. The process includes identifying and removing inaccurate and irrelevant data, dealing with the missing data, removing the duplicate data, etc. Thus, eliminating the major inconsistencies and making the data more efficient to work with.
In this article, we list down 10 datasets for beginners, which can be used for data cleaning practice or data preprocessing.
2020-02-21 09:30:00+00:00 Read the full story…
Weighted Interest Score: 5.2324, Raw Interest Score: 1.8160,
Positive Sentiment: 0.0726, Negative Sentiment 0.0726
AI Resistance is Futile!
We had our first 2020 meetings of Nordic Finance Innovation last week. The theme was digital transformation and its implementation, and was co-hosted by our partner Swedbank. One of the slides struck me as particularly noteworthy. It came from a presentation by Stephan Erne, Chief Digital Officer at Handelsbanken, in reference to artificial intelligence (AI).
Tieto surveyed 3,659 people in Sweden, Norway and Finland in 2019, to understand the general public’s views on the development and use of AI in different areas. The survey consists of two parts, the first part is focusing on industries and occupations, and the second part covers ethical considerations. Notably, most people aren’t too worried about AI’s impact on society. Only a third are really concerned.
2020-02-20 07:13:18+00:00 Read the full story…
Weighted Interest Score: 4.5959, Raw Interest Score: 1.6647,
Positive Sentiment: 0.1189, Negative Sentiment 0.1585
Data Sourcebook (Winter 2019) Issue
From modern data architecture and hybrid clouds, to data science and machine learning, Data Sourcebook is your guide to the latest technologies and strategies in managing, governing, securing, integrating, governing and analyzing data today. Download your copy today to learn about the latest trends, innovative solutions and real-world insights from industry experts on pressing challenges and opportunities for IT leaders and practitioners.
2020-02-19 00:00:00 Read the full story (PDF behind registration wall)…
Weighted Interest Score: 4.2683, Raw Interest Score: 2.0408,
Positive Sentiment: 0.4082, Negative Sentiment 0.4082
Why AI companies don’t always scale like traditional software startups
At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process.
Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we’re not so sure.
We are huge believers in the power of AI to transform business: We’ve put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don’t have the same economic construction as software businesses. At times, they can even look more like traditional services companies. In particular, many AI companies have:
2020-02-22 00:00:00 Read the full story…
Weighted Interest Score: 4.1357, Raw Interest Score: 1.7738,
Positive Sentiment: 0.2690, Negative Sentiment 0.2052
Equipping the Enterprise for Deep Learning: What IT Leaders Need to Know
Deep learning is a form of artificial intelligence that utilizes neural networks, which are computing systems inspired by the human brain and nervous system — essentially a multi-layered “mesh” architecture. Neural networks are not new, but their use in tackling machine learning problems has become so specialized and valuable, it has emerged as the discipline of deep learning. The magic of DL models is in how well they handle data with a huge number of input variables and/or very complex relationships between input variables.
Performance: Deep Learning vs. Machine Learning : When the number of input variables and the complexity of relationships between them are very great, deep learning techniques outperform traditional machine learning. This is often the case with image classification, natural language processing, and complex anomaly detection. For example, a relatively common DL model for image classification takes as input 150,000 values (per image!) and predicts one of 20,000 image categories. This would be extremely hard to handle with other ML techniques. DL models are also commonly used for natural language processing (NLP) and complex anomaly detection, such as the detection of fraud and manufacturing defects.
2020-02-19 00:00:00 Read the full story…
Weighted Interest Score: 4.0110, Raw Interest Score: 2.4096,
Positive Sentiment: 0.3109, Negative Sentiment 0.0972
9 Free E-Books One Must Read To Learn Big Data In 2020
Currently, organisations have been dealing with a huge amount of data, which are both structured and unstructured. According to research, the Hadoop big data analytics market is forecasted to grow at a CAGR of 40% over the next four years. It is one of the biggest reasons behind the rapid industry growth.
In this article, we list down 9 free e-books to learn big data.
- Big Data Now
- Cloudera Impala
- Data Mining and Analysis
- Data-Intensive Text Processing with MapReduce
- Disruptive Possibilities: How Big Data Changes Everything
- Hadoop Explained
- Machine Learning and Big Data
- Migrating Big Data Analytics into the Cloud
- Real-Time Big Data Analytics: Emerging Architecture
2020-02-24 06:27:30+00:00 Read the full story…
Weighted Interest Score: 3.9405, Raw Interest Score: 1.9836,
Positive Sentiment: 0.1488, Negative Sentiment 0.0248
Data and AI in Banking: More Hype Than Reality
Hardly a week goes by without some major consultancy or industry publication talking about how data and AI will transform banking. Effectively leveraged, data and advanced analytics can cut costs, enhance customer experiences, reduce risks and improve returns. Despite the hype, the reality is still discouragingly modest.
Over the past year, the Digital Banking Report has conducted several research studies on the deployment and potential impact of data and artificial intelligence on the banking industry. We have found that the improved use of data and advanced analytics can improve customer experiences, generate better marketing results, streamline deposit and lending operations, increase consumer engagement, support innovation, and be a foundation for digital transformation.
Being a data-driven financial institution is no longer optional (if it ever was). In every industry, winners will be determined by how well data and AI can be used for the benefit of the consumer. Big tech firms such as Google, Apple, Facebook and Amazon (GAFA) are setting the pace, delivering experiences that are improving valuations and providing the foundation for entry into financial services. Fintech firms and non-traditional banking challengers are using data and insights to steal business from legacy banks and credit unions.
2020-02-24 00:05:59+00:00 Read the full story…
Weighted Interest Score: 3.7181, Raw Interest Score: 1.7921,
Positive Sentiment: 0.4630, Negative Sentiment 0.2240
How Big Data Has Changed the Financial Industry
The accessibility and value of consumer data has grown substantially in the past several years. These days, nearly every company bigger than a “mom and pop” shop works to gather and analyze terabytes of data from their customers, hoping to better understand and serve them while one-upping the competition.
In the financial industry, these efforts are particularly intense. Data has the power to shape not only financial decisions (like how and when…
2020-02-21 08:10:39+00:00 Read the full story…
Weighted Interest Score: 3.5833, Raw Interest Score: 1.7226,
Positive Sentiment: 0.5098, Negative Sentiment 0.3340
Is The Recent Criticism For OpenAI by MIT Technology Review Unfair?
OpenAI had earned plenty of plaudits for its transparent and collaborative culture, but the research organization received a drubbing in MIT Technology Review for allegedly breaching the principles it was founded upon. The caustic article exposed a misalignment between the startup’s magnanimous mission and how it operates behind closed doors.
Although some doubts were raised about its mission at the time of Microsoft’s billion-dollar investment …
2020-02-21 05:49:54+00:00 Read the full story…
Weighted Interest Score: 3.3568, Raw Interest Score: 1.5335,
Positive Sentiment: 0.2914, Negative Sentiment 0.2300
AI is not just another technology project
AI, unlike any other initiative is a business transformation enabler and not another technology system implementation that business users need to be trained on. Traditionally, businesses choose either the classic waterfall approach of linear tasks, or the agile approach, where teams review and evaluate solutions as they are tested out.
In contrast, implementing AI technology requires a different approach altogether. AI requires that you look at a problem and see if there’s a way to solve it by reframing the business process itself. Instead of solving a problem with a 10-step strategy, is there a way to cut it down to six steps using data already available or by using new types of untapped internal or publicly available data and applying AI to it? A study by IDC last year found that 60% of organizations reported changes in their business model that were associated with AI adoption.
2020-02-23 00:00:00 Read the full story…
Weighted Interest Score: 3.3073, Raw Interest Score: 1.1809,
Positive Sentiment: 0.2191, Negative Sentiment 0.3531
10 Best Machine Learning Engineering Practices For A Better Product
Machine learning is the hottest topic in the industry. Therefore, they are one of the highest-paid professionals in the industry. ML and its services are only going to extend their influence and push the boundaries to new realms of the technology revolution. However, deploying ML comes with great responsibility. The black box modeling, though is shedding off its black box reputation, it is crucial to establish trust in both in-house teams and stakeholders.
This can be done by practising a few routines that have been tested at the heart of Google AI research departments. Here are a few best practices, which can help ML engineers in a hassle-free model building…
2020-02-24 09:09:37+00:00 Read the full story…
Weighted Interest Score: 3.2681, Raw Interest Score: 1.7572,
Positive Sentiment: 0.3232, Negative Sentiment 0.2020
Phyto Launches Phyto III Cannabis Focused Venture Capital Fund
Want exposure to cannabis but need some professional advice?
Don’t want to go one toke over the line?
Then consider Phyto Partners, a cannabis investment fund, is launching a third cannabis-focused private equity fund modeled after its first two funds, Phyto Partners I, LP and Phyto II, LP. Phyto Partners plans to leverage its early mover advantage and industry expertise to source early and later stage privately held companies that solve critic…
2020-02-20 15:26:01+00:00 Read the full story…
Weighted Interest Score: 3.1646, Raw Interest Score: 1.6275,
Positive Sentiment: 0.3165, Negative Sentiment 0.2260
Companies Bringing AI Training Inside; Udacity Well-Positioned
Companies are taking AI training into their own hands, hiring outside firms to help their employees to learn about AI and often picking up the expense.
Training is a big market. The annual North American workplace training market is estimated to be $169 billion, according to an estimate on Statista. Spending on annual workplace training averaged $83 billion from 2012 to 2019. The share of US companies that partially or fully outsource training is 53%.
Royal Dutch Shell could be a model. The company is expanding an online program that teaches AI skills as part of an effort to cut costs, improve business processes, and generate revenue, according to a recent account in WSJ Pro. Of its 82,000 employees, about 2,000 have expressed interest or been approached by management about taking AI courses through Udacity, the online education company. These include petroleum engineers, chemists, and geophysicists.
2020-02-21 12:21:40+00:00 Read the full story…
Weighted Interest Score: 3.1614, Raw Interest Score: 1.3581,
Positive Sentiment: 0.1509, Negative Sentiment 0.1358
Data Science in 30 Minutes: Medical Metrics that Matter – The Partnership Between Data Science and the Medical Field
The medical field has been one of the fastest adopters of new data science technology. Finding new ways to treat and manage patient health has become a growing industry for data science.
On Wednesday, February 19th, at 5PM ET, we chatted with Bill Lynch, lead data scientist at NeuroFlow, as he discussed the way his team and company are revolutionizing the medical field with their tools. NeuroFlow has built natural language processing tools and other predictive analytic…
2020-02-20 15:18:59-05:00 Read the full story…
Weighted Interest Score: 3.0669, Raw Interest Score: 1.9143,
Positive Sentiment: 0.6381, Negative Sentiment 0.1823
Okera Enhances Automatic Discovery of Sensitive Data Using Machine Learning
According to a recent press release, “Okera announced today version 2.0 of its secure data access platform. The new version uses machine learning to enhance the automatic discovery of sensitive data such as social security numbers and credit card numbers so that organizations are able to protect their consumers’ data and comply with data privacy regulations like GDPR and CCPA. With a visual policy builder, Okera’s secure data access platform allows data owners and stewards to easily create policies that can be enforced dynamically now on Microsoft Azure Data Lake Storage Gen2 in addition to the previously available support of ADLS Gen1 and Amazon S3. Okera is further enhancing its ecosystems by adding support for AWS Glue Data Catalog.”
2020-02-21 08:05:52+00:00 Read the full story…
Weighted Interest Score: 3.0547, Raw Interest Score: 1.7723,
Positive Sentiment: 0.5908, Negative Sentiment 0.1611
Making Better Data-Driven Decisions
Is your organization investing heavily in data, yet not necessarily making better decisions or seeing meaningful results? Research shows that companies are spending massive amounts on data and analytics. Yet, as many as 85% of big data projects fail.
Part of the problem is that in this era of data, the numbers on a computer screen or in a report take on a special air of authority. Users rarely ask where the data came from, how it’s been modified, or whether it is fit for its intended purpose.
2020-02-26 18:00:00+00:00 Read the full story…
Weighted Interest Score: 3.0111, Raw Interest Score: 1.6640,
Positive Sentiment: 0.3170, Negative Sentiment 0.3962
BEAT THE STOCK MARKET WITH MACHINE LEARNING: the Lazy Strategy
Is it possible to have a machine learning model learn the differences between stocks that perform well and those that don’t, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?
2020-02-23 15:05:42.582000+00:00 Read the full story…
Weighted Interest Score: 3.0042, Raw Interest Score: 1.5617,
Positive Sentiment: 0.2477, Negative Sentiment 0.2800
Data Science Infrastructure and MLOps
Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris, Edouard Harris and Russell Pollari. Together, they run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:
You train your model. You check its performance with a validation set. You tweak its hyperparameters, engineer some features and repeat. Finally, you try it out on a tes…
2020-02-23 16:42:15.205000+00:00 Read the full story…
Weighted Interest Score: 2.9930, Raw Interest Score: 1.6650,
Positive Sentiment: 0.0999, Negative Sentiment 0.2331
Explainable AI Needs More Humans
In the midst of the technical jargon of LIME, Shap and the rest, you can forget that the goal is explaining something to a person.
For a lot of people, explainable or interpretable AI or ML means layering a new set of algorithms on top of an older set of algorithms to better understand the older set of algorithms output. Hence, the discussion around interpretable ML can sometimes revolve…
2020-02-24 05:32:36.074000+00:00 Read the full story…
Weighted Interest Score: 2.9632, Raw Interest Score: 1.0177,
Positive Sentiment: 0.0299, Negative Sentiment 0.2394
Google launches TensorFlow library for optimizing fairness constraints
Google AI today released TensorFlow Constrained Optimization (TFCO), a supervised machine learning library built for training machine learning models on multiple metrics and “optimizing inequality-constrained problems.”
The library is designed to help address issues like fairness constraints and predictive parity and help machine learning practitioners better understand things like true positive rates on residents of certain countries, or recall illness diagnoses depending on age and gender.
2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.9263, Raw Interest Score: 1.5263,
Positive Sentiment: 0.5088, Negative Sentiment 0.1696
Top States for Technology Salaries and Growth
The 2020 edition of the Dice Salary Report revealed some key things about the tech industry and the technologists who work in it. Nationally, average annual pay within the tech industry hit $94,000 last year—just a 1.3 percent increase from 2018.
But that’s not the whole story. Salary can vary wildly from state to state, driven by a huge number of factors—everything from the presence of a major tech hub to the average cost of living (which can have a substantial impact on pay). In that spirit, we also broke down average pay on a state-by-state basis; these are the states for which we received a significant number of responses:
2020-02-20 00:00:00 Read the full story…
Weighted Interest Score: 2.8776, Raw Interest Score: 1.8750,
Positive Sentiment: 0.1677, Negative Sentiment 0.0762
Google’s AI drops ‘man’ and ‘woman’ gender labels to avoid possible bias
Google has announced that its image recognition AI will no longer identify people in images as a man or a woman, reports Business Insider. The change was revealed in an email to developers who use the company’s Cloud Vision API that makes it easy for apps and services to identify objects in images.
In the email, Google said it wasn’t possible to detect a person’s true gender based simply on the clothes they were wearing. But Google also said that they were dropping gender labels for another reason: they could create or reinforce biases. From the email:
Given that a person’s gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias.
2020-02-20 07:48:28 Read the full story…
Weighted Interest Score: 2.8524, Raw Interest Score: 1.4819,
Positive Sentiment: 0.0549, Negative Sentiment 0.2195
Insurance Companies Using AI to Build Safety Systems, Optimize Rates
Leading insurance companies in the $500 billion/year insurance industry are studying what types of ML applications to try to gain a business advantage, and startups are using AI to disrupt the industry.
Safety is a big focus, timely considering that motor-vehicle fatalities in 2016 peaked at 40,200; the highest amount recorded in nearly a decade. The estimated healthcare costs to people injured in car crashes totaled over $80…
2020-02-21 12:25:56+00:00 Read the full story…
Weighted Interest Score: 2.7646, Raw Interest Score: 1.2701,
Positive Sentiment: 0.3629, Negative Sentiment 0.3402
Gurucul Introduces Platform for Hunting Security Threats
Gurucul, a provider of unified security and risk analytics technology, is introducing automated intelligent threat hunting that uses artificial intelligence (AI) and machine learning (ML) to detect behaviors associated with cyber attacks and data breaches.
“One of the biggest challenges associated with threat hunting is the manual labor involved in piecing together data from various sources to trace the origin, tactics and techniques across different stages of an attack,” said Nilesh Dherange, CTO of Gurucul. “By combining link analysis and chaining, Gurucul automatically co…
2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.7569, Raw Interest Score: 1.5993,
Positive Sentiment: 0.1263, Negative Sentiment 0.7155
Automation Anywhere Announces World’s First Integrated Process Discovery Solution
grated artificial intelligence (AI)-driven process discovery solution that discovers business processes and with one-click creates bots to automate them. Automation Anywhere Discovery Bot uses AI and machine learning to automatically capture and analyze user actions to uncover common, repetitive process steps as employees navigate between business applications. It then prioritizes automation opportunities by potential return on investment (ROI) and develops RPA bots – accelerating the process automation journey for organizations. Research by Automation Anywhere shows that nearly 80 percent of manual, repetiti…
2020-02-21 08:15:57+00:00 Read the full story…
Weighted Interest Score: 2.7144, Raw Interest Score: 1.9053,
Positive Sentiment: 0.1633, Negative Sentiment 0.1089
Artificial Intelligence Ushers in a New Era of Cost-Effective Clinical Trials
Contributed Commentary by James Streeter, Global Vice President Life Sciences Product Strategy, Oracle Health Sciences
Clinical trials have changed significantly over the past several years. As drugs and devices—and the conditions they are trying to impact—have become increasingly more complex, so has the design and structure of clinical trials. But protocols are costly to change and identifying and enrolling the right patient cohorts is also no…
2020-02-21 12:30:28+00:00 Read the full story…
Weighted Interest Score: 2.6958, Raw Interest Score: 1.2726,
Positive Sentiment: 0.1985, Negative Sentiment 0.2802
Want to get more from your data? Stop focusing on efficiency
Ever since Henry Ford came up with the idea of using moving assembly lines to build automobiles faster at lower cost and at higher quality, efficiency has been the driving force in industry.
Unfortunately, it’s the wrong approach for modern businesses. We’re no longer in an industrial economy. Today, information powers our world. Efficiency drives profit in the short term, but making it your primary focus doesn’t drive innovation; it opens the d…
2020-02-23 00:00:00 Read the full story…
Weighted Interest Score: 2.6771, Raw Interest Score: 1.5008,
Positive Sentiment: 0.8543, Negative Sentiment 0.1847
Britain’s top cop calls for law on police use of AI
Britain’s most senior police officer on Monday called on the government to create a legal framework for police use of new technologies such as artificial intelligence.
Speaking about live facial recognition, which police in London started using in January, London police chief Cressida Dick said that she welcomed the government’s 2019 manifesto pledge to create a legal framework for the police use of new technology like AI, biometrics and DNA.
“The best way to ensure that the police use new and emerging tech in a way that has the country’s support is for the …
2020-02-24 13:18:18+00:00 Read the full story…
Weighted Interest Score: 2.6520, Raw Interest Score: 1.3583,
Positive Sentiment: 0.1294, Negative Sentiment 0.1294
Government must act fast so police can use AI without undermining public trust
Public attention is increasingly focussed on the regulation of police technology. Recent debate has centred around live facial recognition (LFR), following the Met Police’s decision to deploy LFR technology on the streets of London. Proponents argue that LFR will enhance the police’s ability to detect and prevent crime, by enabling officers to more efficiently locate wanted individuals. Privacy campaigners argue that these technologies present a …
2020-02-22 00:00:00 Read the full story…
Weighted Interest Score: 2.6495, Raw Interest Score: 1.0667,
Positive Sentiment: 0.3009, Negative Sentiment 0.5197
Leaked Document Shows How Big Companies Buy Credit Card Data on Millions of Americans
Yodlee, the largest financial data broker in the U.S., sells data pulled from the bank and credit card transactions of tens of millions of Americans to investment and research firms, detailing where and when people shopped and how much they spent. The company claims that the data is anonymous, but a confidential Yodlee document obtained by Motherboard indicates individual users could be unmasked.
The findings come as multiple Senators have urged…
2020-02-19 15:47:10+00:00 Read the full story…
Weighted Interest Score: 2.6489, Raw Interest Score: 1.2696,
Positive Sentiment: 0.0944, Negative Sentiment 0.1373
Four Artificial Intelligence Use Cases in 2020
As the new year and decade have dawned, the buzz surrounding artificial intelligence and its impact in 2020 and beyond shows no signs of slowing down.
AI has now been incorporated into the everyday life of consumers in the developed world, largely driven by the emergence of virtual assistants such as the Alexa, Siri, and Google Assistant ecosystems of devices. This would not have been possible without the maturation of technologies such as voice and image recognition, which cont…
2020-02-24 08:30:01+00:00 Read the full story…
Weighted Interest Score: 2.5952, Raw Interest Score: 1.1274,
Positive Sentiment: 0.2392, Negative Sentiment 0.1025
Data Lake Modernization for Speed, Scale and Agility
DBTA ROUNDTABLE WEBINAR THURSDAY, MARCH 19, 2020 – 11:00 am PT / 2:00 pm ET
Data lake adoption has more than doubled over the past three years. Currently in use by 45% of DBTA subscribers to support data science, data discovery and real-time analytics initiatives, data lakes are still underpinned by Hadoop in many cases, although cloud-native approaches are on the rise. The technologies and best practices surrounding data lakes continue to evolve, as well as the challenges, from data governance and security, to integration and architecture. Join us for a special roundtable webinar on March 19th to learn …
2020-03-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5355, Raw Interest Score: 1.6227,
Positive Sentiment: 0.2028, Negative Sentiment 0.1014
AI Weekly: Why a slow movement for machine learning could be a good thing
In 2019, the number of published papers related to AI and machine learning was nearly 25,000 in the U.S. alone, up from roughly 10,000 in 2015. And NeurIPS 2019, one of the world’s largest machine learning and computational neuroscience conferences, featured close to 2,000 accepted papers from thousands of attendees.
There’s no question that the momentum reflects an uptick in publicity and funding — and correspondingly, competition — within the AI research community. But some academics suggest the relentless push for progress might be causing more harm than good.
2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.4176, Raw Interest Score: 1.4201,
Positive Sentiment: 0.2517, Negative Sentiment 0.4134
ProBeat: AI is helping Microsoft rethink Office for mobile
e app is not just for consuming content and maybe a little light editing on the side, but actually creating content on the go. Most interestingly, a lot of these features fundamentally require AI and machine learning to achieve this new mobile productivity paradigm.
Microsoft has been adding AI-driven features to its once most profitable product line for years now — we did a recap of just a handful last year. This week’s Office launch, however, showed Microsoft’s embrace of AI as not merely augmenting what you can already do with the productivity suite, but added new use cases altogether. Most of the new fea…
2020-02-21 00:00:00 Read the full story…
Weighted Interest Score: 2.4014, Raw Interest Score: 1.1773,
Positive Sentiment: 0.1472, Negative Sentiment 0.0981
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