AI & Machine Learning News. 18, May 2020

The Artificial Intelligence and Machine Learning Newsletter by CloudQuant

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?


AI Generates Music with Singing [OpenAI Jukebox]

Read more at OpenAI Jukebox

Intel Capital commits $132 million to 11 AI startups

Intel today announced that Intel Capital, its global investment organization, committed a total of $132 million to 11 startups focused on AI, automation, and chipset design. It follows a year in which the firm invested $466 million in 36 new companies (and 35 follow-on investments) and led 72% of its deals through 22 successful exits. In 2020, Intel Capital says it’s on track to invest around the same amount — between $300 million and $500 million — in startups specializing in AI, with a particular focus on intelligent edge devices and network transformation.

Intel doubling down on AI and machine learning is business as usual. During an earnings call late last year, CEO Bob Swan said the company generated $3.8 billion in AI-based revenue in 2019, and that he anticipates the market opportunity will reach $25 billion by 2024. To position itself for growth, Intel recently acquired Habana Labs, an Israel-based developer of programmable AI and machine learning accelerators for cloud datacenters, as well as Moovit, a mobility startup that could be central to Intel subsidiary Mobileye’s plans for a robo-taxi service.

2020-05-12 06:00:00+00:00 Read the full story…

CloudQuant Thoughts : Intel investing its money wisely in AI and ML.

USPTO pronounces “AI cannot Invent Patents”

In a landmark decision published on April 27, 2020, the US Patent and Trademark Office has affirmed its stand on the question, “Can AI be the inventor?” The USPTO has denied acknowledging AI as an inventor.

The question of providing inventorship to AI arose, when on July 29, 2019, Stephen Thaler, as assignee, filed a patent application listing the inventor’s given name as “DABUS” and family name as “Invention generated by artificial intelligence.” DABUS – the “inventor” – is a “the creativity machine,” a series of neural networks created by Thaler. The USPTO issued a Notice to File Missing Parts because the application did “not identify each inventor by his or her legal name.”

The USPTO has now reaffirmed its stand and denied the petition to vacate the Notice of Missing Parts of the Application No.: 16/524,350 (the ‘350 Application), titled “Devices and Methods for Attracting Enhanced Attention (DABUS).”

2020-05-12 18:00:11 Read the full story…

CloudQuant Thoughts : This decision may make a number of large businesses re-think their policies towards AI and ML. Probable first step will be a dramatic increase in the strength of Non Disclosure orders.

Decision Tree vs. Random Forest – Which Algorithm Should you Use? A Simple Analogy to Explain Decision Tree vs. Random Forest

Let’s start with a thought experiment that will illustrate the difference between a decision tree and a random forest model.

Suppose a bank has to approve a small loan amount for a customer and the bank needs to make a decision quickly. The bank checks the person’s credit history and their financial condition and finds that they haven’t re-paid the older loan yet. Hence, the bank rejects the application. But here’s the catch – the loan amount was very small for the bank’s immense coffers and they could have easily approved it in a very low-risk move. Therefore, the bank lost the chance of making some money.

Now, another loan application comes in a few days down the line but this time the bank comes up with a different strategy – multiple decision-making processes. Sometimes it checks for credit history first, and sometimes it checks for customer’s financial condition and loan amount first. Then, the bank combines results from these multiple decision-making processes and decides to give the loan to the customer. Even if this process took more time than the previous one, the bank profited using this method. This is a classic example where collective decision making outperformed a single decision-making process. Now, here’s my question to you – do you know what these two processes represent?

2020-05-11 19:53:21+00:00 Read the full story…
Weighted Interest Score: 5.0133, Raw Interest Score: 2.6527,
Positive Sentiment: 0.1813, Negative Sentiment 0.1908

CloudQuant Thoughts : A very nice clean clear comparison of  Decision Trees and Random Forests.

Our weird behavior during the pandemic is messing with AI models

It took less than a week at the end of February for the top 10 Amazon search terms in multiple countries to fill up with products related to covid-19. You can track the spread of the pandemic by what we shopped for: the items peaked first in Italy, followed by Spain, France, Canada, and the US. The UK and Germany lag slightly behind. “It’s an incredible transition in the space of five days,” says Rael Cline, Nozzle’s CEO. The ripple effects have been seen across retail supply chains.

But they have also affected artificial intelligence, causing hiccups for the algorithms that run behind the scenes in inventory management, fraud detection, marketing, and more. Machine-learning models trained on normal human behavior are now finding that normal has changed, and some are no longer working as they should.

How bad the situation is depends on whom you talk to. According to Pactera Edge, a global AI consultancy, “automation is in tailspin.” Others say they are keeping a cautious eye on automated systems that are just about holding up, stepping in with a manual correction when needed.

What’s clear is that the pandemic has revealed how intertwined our lives are with AI, exposing a delicate codependence in which changes to our behavior change how AI works, and changes to how AI works change our behavior. This is also a reminder that human involvement in automated systems remains key. “You can never sit and forget when you’re in such extraordinary circumstances,” says Cline.

2020-05-11 00:00:00 Read the full story…
Weighted Interest Score: 2.3098, Raw Interest Score: 0.9174,
Positive Sentiment: 0.1189, Negative Sentiment 0.1869

CloudQuant Thoughts : Already pulled this one out for the Alternative Data Blog Post last Thursday but it sits better here. A very interesting article with lots of quotables! “This is also a reminder that human involvement in automated systems remains key”, AI and ML are extremely powerful tools but one only has to try to send a simple text using SIRI, a quite narrow AI/ML task these days, to witness how easily it can go wrong. One interviewee described AI based systems as “fragile”, they are certainly not “set and forget”. The section about high speed online advertising pricing was also very interesting. One of my colleagues worked in that environment, where online advertisers have algos which bid against each other for ad-space for fractions of a penny in fractions of a second. Having recently witnessed the massive OIL price crash, I can only imagine how out of control these ad markets must be right now. “You need a data science team who can connect what’s going on in the world to what’s going on the algorithms, an algorithm would never pick some of this stuff up”, FOR NOW!

Top Buys In Fixed Income Space According To AI

Our AI suggests these are the top buys in the fixed income space Getty

Our deep learning Artificial Intelligence (“AI”) systems that are studying alternative data like article sentiment, social sentiment, and more, alongside fundamental and price data, has given us some fixed income ideas to buy and sell. With monetary and fiscal stimulus happening at unprecedented levels worldwide, fixed income has had a strong year, as the race to zero (or neg…
2020-05-18 00:00:00 Read the full story…
Weighted Interest Score: 6.6846, Raw Interest Score: 2.4820,
Positive Sentiment: 0.1868, Negative Sentiment 0.0534

CloudQuant Thoughts : Is it surprising that Bond ETFs are seeing inflow when then FED has announced that it is buying Bond ETFs?

Papers With Code

The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.

We believe this is best done together with the community and powered by automation.

We’ve already automated the linking of code to papers, and we are now working on automating the extraction of evaluation metrics from papers.

Read the full story…
CloudQuant Thoughts : Lots of lovely Papers with supporting code, that’s what we like to see. Head over to our Data Set Catalog to see our white papers which include code and access to the data!

NVIDIA launches ‘world’s most advanced AI system’ – the DGX A100

NVIDIA today announced the arrival of its AI system DGX A100, delivering 5 petaflops of AI performance, available now and shipping worldwide. NVIDIA says the first order of the system will be delivered to a lab in the US, which will use the DGX A100’s computing power to ‘better understand COVID-19’. The system integrates eight of the Tensor Core GPUs from the new NVIDIA A100 GPU, also announced today. 

This will provide a whopping 320GB of memory for training large AI datasets. “NVIDIA DGX A100 is the ultimate instrument for advancing AI,” says NVIDIA founder and CEO Jensen Huang. “[It’s] the first AI system built for the end-to-end machine learning workflow — from data analytics to training to inference. “And with the giant performance leap of the new DGX, machine learning engineers can stay ahead of the exponentially growing size of AI models and data.”

2020-05-15 Read the full story…

Weekend Roundup: A.I. Expert Slap-Fight

Artificial intelligence (A.I.) is a complex and sometimes emotionally fraught issue. Experts worry that A.I. platforms will begin to display bias that will not only skew results, but have long-term negative effects on everything from facial recognition to hiring. And that’s before you begin to consider how A.I. might lead to the much-fantasized scenario of “killer robots.”

Now Jerome Persati, head of Facebook’s A.I. initiative, is taking Tesla CEO Elon Musk to task over some of those issues. As reported by The Next Web, Persati says that Musk, who regularly predicts that Tesla vehicles will become almost completely autonomous, “has no idea what he is talking about when he talks about A.I.”

2020-05-15 00:00:00 Read the full story…
Weighted Interest Score: 1.0970, Raw Interest Score: 0.7700,
Positive Sentiment: 0.1925, Negative Sentiment 0.2695

Inside the quest for a new COVID-19 test: Microsoft, Adaptive Biotech and the hidden power of immunity (Podcast)

In the realm of diagnostic tests for COVID-19, there are two main approaches: PCR tests, which detect the presence of the live virus; and serology tests, which detect antibodies that indicate whether someone has recovered from the disease.

But could there be a third way? Two companies in the Seattle region, Microsoft and Adaptive Biotechnologies, are on a quest to create a better diagnostic test.

The two Seattle-area companies are using machine learning to search for the unique signature associated with COVID-19 in the specialized cells that determine the human immune system’s response to the disease. Once that signature is identified, they say, it could lead to a new test that would identify the tell-tale signs of the disease in others, providing a new form of diagnosis.

2020-05-15 16:22:00+00:00 Read the full story…
Weighted Interest Score: 1.0635, Raw Interest Score: 0.8638,
Positive Sentiment: 0.1728, Negative Sentiment 0.2016

AI helped Facebook crack down on 68% more hate posts in Q1

Facebook says it took down 68% more hate posts in the first quarter of 2020 than it did in the last quarter of 2019. The company says that the increase is due mainly to the marked improvements in the machine learning systems it uses to track down hateful content on its network.

The result is part of Facebook’s Community Standards Enforcement Report, released today, which details the company’s tactics and success rates in enforcing its community guidelines.

On a call with reporters on Tuesday, CEO Mark Zuckerberg said that the company has been relying more heavily on its AI detection systems since early March, when it sent most of its human content moderators home to self-quarantine. The AI systems, he said, now detect 90% of hate speech posts on the platform before they’re reported by a user.

2020-05-12 18:00:11 Read the full story…
Weighted Interest Score: 1.0417, Raw Interest Score: 0.6635,
Positive Sentiment: 0.1896, Negative Sentiment 0.4265

How AI is Changing the Mobility Landscape

Smart cities around the world strive to provide more efficient and greener transportation options. Transportation options like Connected Autonomous Vehicles (CAV) and drone deliveriesare the next technologies to dominate the mobility landscape.

The way people and goods move will have to change dramatically due to the constantly increasing traffic, traffic-induced noise and pollution, and limited availability of space in urban areas.

Organizations use technologies like machine learning, artificial intelligence, and data analytics to identify, predict, and solve mobility challenges. Artificial intelligence technology enables companies and cities to transition to autonomous mobility — highly individualized and environmental-friendly systems.

Read the full story…

Accelerated ETL With Spark and RAPIDS

Extract, transform, and load (ETL). Those are three words that when placed side-by-side in nearly any order strike fear into people across all levels of business. ETL is perhaps one of the most frustrating topics in existence because without it, downstream data processing, such as analytics and machine learning, cannot really function. It involves getting the data from a data source, changing the formatting in some logical way as to benefit the downstream process, and then loading it into the next storage location for later use.

There is now a slightly broader category beyond just ETL and that is data preparation, which includes some of the more standard concepts such as data standardization and cleansing. These concepts are often lumped together with the “transform” part of ETL.

As data sizes have grown over the last decade, so has the amount of time it takes to run ETL processes to support the myriad downstream workloads. A decade ago, most people were only thinking about making their KPI dashboards faster. As time rolled forward, they started to think about getting more intelligent analytics out of their data, and the data sizes quickly grew from gigabytes to terabytes.
2020-05-18 00:00:00 Read the full story…
Weighted Interest Score: 2.9661, Raw Interest Score: 1.9221,
Positive Sentiment: 0.2996, Negative Sentiment 0.0999

For American Airlines, Machine Learning Solves an Air Cargo Conundrum

“No-shows cost us millions in lost revenue, and many times they can result in us needlessly turning away other critical shipments when we could have otherwise carried them,” said Chris Isaac, managing director of American Airlines Cargo Revenue Management. “Being able to firm up a flight’s bookings in advance allows us to recapture space that will go unused and provide it to others who need it.”

American Airlines decided to create a machine learning model that analyzes each customer’s booking to predict the likelihood of a no-show shipment. The model was trained with a year’s worth of cargo data – half a million records, each with around 20 variables – using an open-source, GPU-accelerated ML package called H2O4GPU.

2020-05-14 00:00:00 Read the full story…
Weighted Interest Score: 1.6387, Raw Interest Score: 1.1728,
Positive Sentiment: 0.1564, Negative Sentiment 0.1955

Why you should learn CatBoost now

As I was designing the content for a training on Machine Learning, I ended up digging through the documentation of CatBoost. And there I was, baffled by this immensely capable framework. Not only does it build one of the most accurate model on whatever dataset you feed it with — requiring minimal data prep — CatBoost also gives by far the best open source interpretation tools available today AND a way to productionize your model fast.

That’s why CatBoost is revolutionising the game of Machine Learning, forever. And that’s why learning to use it is a fantastic opportunity to up-skill and remain relevant as a data scientist. But more interestingly, CatBoost poses a threat to the status quo of the data scientist (like myself) who enjoys a position where it’s supposedly tedious to build a highly accurate model given a dataset. CatBoost is changing that. It’s making highly accurate modeling accessible to everyone.  pip install catboost
2020-05-18 13:18:22.669000+00:00 Read the full story…
Weighted Interest Score: 2.6179, Raw Interest Score: 1.2973,
Positive Sentiment: 0.1483, Negative Sentiment 0.1235

Report Finds Technology Will Enhance Finance Jobs

Technology has enhanced most American careers in finance, according to a new paper. According to a new report entitled “The Future of Trading: the People” produced by Refinitiv in conjunction with Greenwich Associates, only “4% of Gen Xers and 7% of millennials told us that technology innovation has limited their career opportunities.”

Meanwhile the report found that 80%, “of capital markets professionals believe technology has provided them new career opportunities.” The report continued, “The vast majority of financial professionals feel that technology innovation has, in fact, enhanced their career thus far. Roughly 4 out of 5 finance professionals feel that technology innovation has presented them with new opportunities, and about half say that it has accelerated their career growth. While the positive sentiment is slightly stronger among the digital-native millennial crowd, Gen Xers and baby boomers are similarly excited about the impact of the market’s digitization on their job progression.
2020-05-11 01:39:56+00:00 Read the full story…
Weighted Interest Score: 2.8005, Raw Interest Score: 1.7304,
Positive Sentiment: 0.5464, Negative Sentiment 0.1138

Data Science, ML Platform Leader Board Shuffled

A roster of technology hyper-scalers and a batch of up-and-comers make the latest rankings of the leading data science and AI and machine learning platforms.

Market tracker Omdia’s list of AI and ML development platforms was topped by IBM and Microsoft. IBM (NYSE: IBM) was credited with offering a full-featured “build-deploy-validate-monitor-govern” workflow for machine learning applications. Microsoft’s (NASDAQ: MSFT) automated ML tools were cited for freeing data scientists to focus on organizing data and deploying applications via its Azure cloud.

Rounding out the top five leaders are C3.ai, Datakai and SAS. They were followed by AI development “challengers” Petuum and H2O.ai, with Evolution AI listed as a “follower.”

2020-05-13 00:00:00 Read the full story…
Weighted Interest Score: 4.9047, Raw Interest Score: 2.1610,
Positive Sentiment: 0.0313, Negative Sentiment 0.0313

Northern Trust Deploys AI For Currency Management

Northern Trust announced today it has enhanced its Foreign Exchange (FX) currency management solutions with machine learning models designed to enable greater oversight of thousands of daily data points and help reduce risk throughout the currency management lifecycle. The solution has been developed in conjunction with Northern Trust’s strategic partner Lumint Corporation.

The advanced technology utilized by the Robotic Oversight System (ROSY) for Northern Trust, systematically scans newly arriving, anonymized data to identify anomalies across multi-dimensional data sets. It is built on machine learning models developed by Lumint using a cloud platform that allows for highly efficient data processing.
2020-05-14 10:06:25+00:00 Read the full story…
Weighted Interest Score: 3.7956, Raw Interest Score: 2.6206,
Positive Sentiment: 0.4178, Negative Sentiment 0.0760

CLS Launches Analysis of FX Market Liquidity

The economic impacts of the current global health emergency have profoundly changed the FX environment, and MUFG identified a need for market participants to have additional visibility and insights into liquidity and volatility. Developed from the ground up over a few weeks, these new analytics harness CLS’s robust, aggregated FX market data, MUFG’s aggregated FX order book data and Mosaic Smart Data’s advanced analytics software. The benefit of this collaboration is to provide the FX community with greater transparency into FX market conditions.

Mosaic Smart Data will publish this analysis weekly, providing users with an ongoing, data-driven view into liquidity changes across key currency pairs. Accessible via a dedicated portal and free of charge, the analysis is provided by Mosaic Smart Data’s platform, and the platform’s Natural Language Generation (NLG) technology then generates instant written reports to provide insights on key aspects of the data.

2020-05-18 09:56:01+00:00 Read the full story…
Weighted Interest Score: 3.6611, Raw Interest Score: 1.6018,
Positive Sentiment: 0.3789, Negative Sentiment 0.1895

What Do You Need To Do Before Hiring A Data Scientist?

Artificial Intelligence brings a promise of exponential growth and taking your business to new heights. No wonder there is a lot of excitement around the application of Artificial Intelligence (AI).

Many companies are rushing to hire their first Data Scientist or build a Data Science team right off the bat. Their enthusiasm is understandable as they want to innovate with data and not be out-competed by the market. However, these early missteps and false starts are causing a massive opportunity cost to companies, and Data Scientists are moving on from these companies within just a couple of years.

Here are some recommendations for you to prepare before investing in Data Science function at your company.

2020-05-12 15:09:06+00:00 Read the full story…
Weighted Interest Score: 3.2863, Raw Interest Score: 1.8285,
Positive Sentiment: 0.1441, Negative Sentiment 0.2660

Dashboard Tracks Economic Impact of COVID-19

There has been no shortage of dashboards for tracking the spread of the novel coronavirus. Now, thanks to a partnership between Womply and Harvard University, we have a dashboard that tracks the economic impact of COVID-19 and the lockdown that governments have instituted to fight its spread. Last week, CRM and marketing software developer Womply unveiled the new dashboard, called the Opportunity Insights (OI) Economic Tracker, which is based on billions of data points that “present a daily picture of economic activity,” the company says.

The dashboard displays a range of metrics that demonstrate the economic impact of COVID-19 and the shutdown of businesses. For instance, users can visualize the decline in consumer spending (via credit card data) at the state, county, and select metropolitan areas, or drill down and view aggregated data for categories like unemployment, the number of hours worked at small businesses, and job postings. The data can be sliced by sector (such as education/health services or leisure/hospitality), and compared against state and national averages when drilled in at the county or city level.

2020-05-11 00:00:00 Read the full story…
Weighted Interest Score: 3.2394, Raw Interest Score: 1.5520,
Positive Sentiment: 0.2822, Negative Sentiment 0.2822

Ex-Docker CFO heads up new Seattle finance startup Stratify, with Concur founder as chairman

Former Docker executives Brian Camposano and Steve Singh are behind a stealthy new Seattle-based fintech startup called Stratify.

The company just spun out of Seattle startup studio Madrona Venture Labs (MVL), where Camposano was an entrepreneur-in-residence since March.

Camposano, the former Docker CFO, is the lone employee at Stratify. The company is in its earliest stages and does not have a live website. Camposano said its vision is to “reinvent Strategic Finance, utilizing machine learning and artificial intelligence to provide companies unparalleled real-time insights.”

2020-05-15 13:00:00+00:00 Read the full story…
Weighted Interest Score: 3.1015, Raw Interest Score: 1.8132,
Positive Sentiment: 0.1813, Negative Sentiment 0.1209

Spark 3.0 to Get Native GPU Acceleration

NVIDIA today announced that it’s working with Apache Spark’s open source community to bring native GPU acceleration to the next version of the big data processing framework. With Spark version 3.0, which is due out next month, organizations will be able to speed up all of their Spark workloads, from ETL jobs to machine learning training, without making wholesale changes to their code.

The company says Spark users will be able to be train their machine learning models on the same Spark cluster where they are running extract, transform, and load (ETL) jobs to prepare the data for processing. NVIDIA claims this is a first for Spark, which it says is used by 500,000 data scientists and data engineers around the world.

2020-05-14 00:00:00 Read the full story…
Weighted Interest Score: 3.0248, Raw Interest Score: 1.9188,
Positive Sentiment: 0.2828, Negative Sentiment 0.0404

NYU researchers built an AI tool to predict severe cases of COVID-19

COVID-19 doesn’t create cookie-cutter infections. Some people have extremely mild cases while others find themselves fighting for their lives. Clinicians are working with limited resources against a disease that is very hard to predict. Knowing which patients are most likely to develop severe cases could help guide clinicians during this pandemic.

We are two researchers at New York University that study predictive analytics and infectious diseases. In early January, we realized that it was very possible the new coronavirus in China was going to make its way to New York, and we wanted to develop a tool to help clinicians deal with the incoming surge of cases. We thought predictive analytics—a form of artificial intelligence—would be a good technology for this job. In a general sense, this type of AI looks at existing data to find patterns and then uses those patterns to make predictions about the future. Using data from 53 COVID-19 cases in January and February, we developed a group of algorithms to determine which mildly ill patients were likely become severely ill.
2020-05-18 09:00:45 Read the full story…
Weighted Interest Score: 2.8394, Raw Interest Score: 1.2442,
Positive Sentiment: 0.0518, Negative Sentiment 0.3629

How can businesses use data science?

For both B2B and B2C businesses, the supply chain is an incredible source of data. The businesses that are able to capitalize on the data of their customers, business, and operations have a huge competitive advantage in the market. It is evident that knowledge is power in growing a business. However, most people fail to realize that the data they have is the fuel to generate that power. Most data goes unused due to a limited understanding of how it can help drive the business’s growth and generate positive outcomes.

For instance, a supermarket has been running on loss for a few months. However, this was not the case when they first started. But now, with rising competition, they are facing a lack of customers and enough sales to drive the operations. They have been struggling to keep the business running and cannot figure out the reason for their declining customers. With no significant results from multiple discounts and digital marketing campaigns, shutting down the business might be the only option they have in mind.

However, with data science, the supermarket can study in-depth about their customers, their behaviors, and preferences. This can help them learn and improve several factors such as customer service, product quality, price factors, location, and asset utilization. These factors aid in achieving a successful implementation, cut costs and generate a high return on investment (ROI).

2020-05-18 06:46:41+00:00 Read the full story…
Weighted Interest Score: 2.7789, Raw Interest Score: 1.4584,
Positive Sentiment: 0.3547, Negative Sentiment 0.1774

An Introduction to Machine Learning Libraries for C++

I love working with C++, even after I discovered the Python programming language for machine learning. C++ was the first programming language I ever learned and I’m delighted to use that in the machine learning space!

I wrote about building machine learning models in my previous article and the community loved the idea. I received an overwhelming response and one query stood out for me (from multiple folks) – are there any C++ libraries for machine learning?

It’s a fair question. Languages like Python and R have a plethora of packages and libraries that cater to different machine learning tasks. So does C++ have any such offering?

2020-05-13 19:05:57+00:00 Read the full story…
Weighted Interest Score: 2.7776, Raw Interest Score: 1.3933,
Positive Sentiment: 0.0811, Negative Sentiment 0.0811

Data Analyst Salary: 5 Pressing Questions Answered

What’s a typical data analyst salary? How much can those with a lot of experience and skills potentially earn?

Data analysts are crucial members of many organizations. Executives rely on data analysts’ work product to make vital decisions about the overall direction of the business. On a team level, data analysts also provide those valuable insights that allow developers, engineers, and others to make short-term decisions.

In other words, data analysts can mean the difference between success and failure. But does the average data analyst salary match the role’s actual importance to the organization? That’s a very big and complicated question.

2020-05-13 00:00:00 Read the full story…
Weighted Interest Score: 2.7514, Raw Interest Score: 1.6342,
Positive Sentiment: 0.1668, Negative Sentiment 0.1834

Microsoft chief scientist: Humans and AI work better together than alone

Humans and AI systems work better when they tackle problems together. That’s according to research from Microsoft chief scientist Eric Horvitz, Microsoft Research principal researcher Ece Kamar, and Harvard University student and Microsoft Research intern Bryan Wilder. The paper appears to be one of the first published by Horvitz since Microsoft named him chief scientific officer in March, the first in company history. Horvitz came to Microsoft as a principal researcher in 1993 and led Microsoft Research operations from 2017 to 2020.

The paper released earlier this month studies the performance of human and AI teams working together on two computer vision tasks: Galaxy classification and breast cancer metastasis detection. With the proposed approach, the AI model determines which tasks are best for humans to perform and which are better handled by AI.

The learning strategy is optimized to combine machine predictions and human contributions, with AI focusing on problems difficult for humans and humans tackling problems that can be tough for machines to figure out. Basically, machine predictions made without high levels of accuracy are routed to a human. Researchers say joint training can improve galaxy classification model Galaxy Zoo performance with a 21-73% reduction in loss and deliver an up to 20% performance improvement for CAMELYON16.
2020-05-17 00:00:00 Read the full story…
Weighted Interest Score: 2.4704, Raw Interest Score: 1.1873,
Positive Sentiment: 0.2639, Negative Sentiment 0.2309

‘Superpower marathon’: U.S. may lead China in tech right now — but Beijing has the strength to catch up

  • The U.S. currently leads China in many aspects of technology — but experts caution against the world’s largest economy resting on its laurels, urging instead for cooperation with allies and shifts in domestic policy.
  • China has laid out a number of plans it hopes will propel it to global leadership in areas from 5G to artificial intelligence.
  • Experts point out that the U.S. could tap on alliances and re-orientate domestic policy to increase competitiveness.

The United States might be leading in some areas of its technology race with China — but experts warn against the world’s largest economy resting on its laurels, urging instead for cooperation with allies and shifts in domestic policy. Alongside trade war developments between the U.S. and China, both parties have been embroiled in growing competition to dominate various fields of next-generation technology, such as 5G networks and artificial intelligence (AI).
2020-05-18 00:00:00 Read the full story…
Weighted Interest Score: 2.4152, Raw Interest Score: 1.2857,
Positive Sentiment: 0.1773, Negative Sentiment 0.1552

Python Certifications: 4 Big Questions Answered

Are Python certifications worth obtaining? Python is one of the most widely used programming languages on Earth. Not only is it a popular “generalist” language, but it has crept steadily into highly specialized segments—for example, it’s overtaken R as the data-science language of choice for many companies.

In other words, those interested in Python development can find lots of opportunity to use their skills. But is a certification necessary for a career in Python-related development? That’s a trickier question to answer.

At the moment, Python developers are in high demand. Burning Glass, which collects and analyzes millions of job postings from across the country, projects that Python developer jobs will grow 30.7 percent over the next decade. Currently, time-to-fill open Python developer positions is 38 days, indicating that employers are expending a lot of time and effort to find available candidates.

2020-05-18 00:00:00 Read the full story…
Weighted Interest Score: 2.3942, Raw Interest Score: 1.4536,
Positive Sentiment: 0.1283, Negative Sentiment 0.1069

Hierarchical Classification by Local Classifiers: Your Must-Know Tweaks & Tricks

Have you got a hierarchical classification task that’s just begging for a machine learning model? Have you decided opting for an ensemble of local classifiers, and even decided on the best local-classifier structure for that very task? Is your keyboard all warmed up, fingers ready and raring to go? Well, just a minute now. You might wanna sit back down.

If you’ve read my previous posts on the subject of hierarchical classification, you have definitely got all the basics down. However, there are some important final details you should make yourself acquainted with. Whether it’s how to choose your training examples and feature sets, how to avoid error propagation, or ways to handle classification inconsistencies — this is one post you want to read before implementing your first real-life hierarchical model.
2020-05-18 13:21:34.837000+00:00 Read the full story…
Weighted Interest Score: 2.2163, Raw Interest Score: 0.9639,
Positive Sentiment: 0.2771, Negative Sentiment 0.3614

How Quantum Computing is Being Piloted for Practical Applications

Quantum computing for general-use machines is believed to be a long way off, but savvy tech people in financial services are preparing now.

JPMorgan Chase for the past two years has been a part of IBM’s Q Network, the company’s initiative aimed at advancing quantum computing.

“The astounding progress at the hardware level during the last decade or so brought us to a point where we started thinking, there might be something there, it’s probably a good idea to get into it sooner rather than later so we can see what it means for us,” stated Nikitas Stamatopoulos, JPMorgan Chase’s quantum computing quantitative researcher, in a recent account in ZDNet.

“So if it means something, we’re ready to take advantage of it and if it doesn’t, we need to know that as well,” he added.
2020-05-14 21:30:00+00:00 Read the full story…
Weighted Interest Score: 2.1928, Raw Interest Score: 1.3012,
Positive Sentiment: 0.3133, Negative Sentiment 0.1928

AI-Powered Data Protection Firm Gets $12M in Funding

Dathena, a Singapore-based company that uses AI techniques to detect and secure its clients’ sensitive data, has closed a $12 million Series A round, the company announced yesterday.

Dathena’s roots stretch back to 2011, when HSBC’s Christopher Muffat led the investigation of Swiss Leaks, the massive theft of sensitive data that included over 100,000 names of HSBC clients suspected of being involved in tax evasion at the bank’s Switzerland-based subsidiary. Muffat “understood that the root cause behind the crisis was HSBC’s failure to identify what it needed to protect,” according to Dathena’s website. He eventually came to realize that “all organizations systematically fail to quickly and accurately identify and classify their sensitive information, thereby putting at risk their customers, employees, and shareholders,” the website says.
2020-05-14 00:00:00 Read the full story…
Weighted Interest Score: 2.1780, Raw Interest Score: 1.1650,
Positive Sentiment: 0.1503, Negative Sentiment 0.4885

Running Kafka on Kubernetes the Easy Way

Kubernetes is one of the most popular open source container orchestrators and management APIs. It’s one of the emerging platforms that enables companies to run and manage containerized applications globally. Built to automate deploying, scaling and operating application containers, cloud-native support from AWS, GCP, Azure, it has a growing enterprise support ecosystem. Leveraging Kubernetes to provide tested, repeatable deployment patterns that follow best practices is a win for both developers and the operators.

2020-05-18 00:00:00 Read the full story…
Weighted Interest Score: 2.1005, Raw Interest Score: 1.3242,
Positive Sentiment: 0.4566, Negative Sentiment 0.0457

Navigating the Uneasy Alliance Between Tech Giants and Healthcare Organizations

Tech giants like IBM, Amazon, Google and Microsoft are the latest players in healthcare, striking deals with hospitals to secure access to millions of patient records. According to the Wall Street Journal, 80% of all medical records are now digital, and this trove of data may prove invaluable for tech companies — placing protected health information (PHI) in the hands of big tech could result in algorithms capable of predicting future diagnoses, search tools to quickly locate a patient’s file or customized treatment plans.

But not surprisingly, lawmakers and patients are concerned about how increased data sharing will impact security. Healthcare is already the second-most ransomware-targeted industry (just behind finance) and companies face steep fines for noncompliance with healthcare privacy and security guidelines. In addition to security concerns, privacy experts are worried that tech companies will improperly use patient data for commercial purposes. For example, the reveal of Google’s Project Nightingale, a data storage partnership with Ascension health, caused a public outcry because of the potential for privacy violations.
2020-05-12 11:00:00+00:00 Read the full story…
Weighted Interest Score: 2.0254, Raw Interest Score: 1.2567,
Positive Sentiment: 0.1922, Negative Sentiment 0.4287

Privacy Advocates Calls for Transparency Over Use of Patient Data

With the coronavirus outbreak continuing apace across much the globe, a group of tech firms is responding to a growing demand by governments for citizen information, which is being widely sought after for contact tracing and for analyzing patient data. Now, with the recent revelation that U.S.-based big data firm Palantir will be joining the scene, privacy advocates have been left up in arms over what the implications might be for civil liberties.

Concerns began to surface in mid-April, when it emerged from reports and leaked documents that Palantir had begun working alongside the UK’s National Health Service (NHS) in order to analyze patient data using its Foundry software, which turns a data platform into a data store tailored to the coronavirus pandemic.

2020-05-13 16:00:00+00:00 Read the full story…
Weighted Interest Score: 1.9787, Raw Interest Score: 1.1796,
Positive Sentiment: 0.2473, Negative Sentiment 0.3234

Big Data Career Notes: May 2020 Edition

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the big data community. Whether it’s a promotion, new company hire, or even an accolade, we’ve got the details. Check in each month for an updated list and you may even come across someone you know, or better yet, yourself!

2020-05-15 00:00:00 Read the full story…
Weighted Interest Score: 1.9656, Raw Interest Score: 1.1304,
Positive Sentiment: 0.4587, Negative Sentiment 0.0328

Data Discovery and Data Mapping: Are These Automated Software Technologies Effective for LGPD Compliance?

Considering that privacy issues are causing the emergence of laws and regulations, companies are seeking to comply with the requirements to protect third-party personal data against loss of confidentiality, integrity, and availability.

To reach their goals, companies need to begin with identifying gaps; that is, finding any deficiencies in information security controls. Those gaps can cause the occurrence of significant incidents, which can lead…
2020-05-11 11:00:00+00:00 Read the full story…
Weighted Interest Score: 1.7279, Raw Interest Score: 0.9734,
Positive Sentiment: 0.0993, Negative Sentiment 0.1788

How Big Data And Machine Translation Combine To Fight COVID-19

Few if any events in history have brought the importance of big data to popular awareness more than the COVID-19 pandemic. Statistics gathered from around the world are driving public policy and shaping private behavior. Here we’ll focus on the linguistic dimension of this global struggle to communicate essential information both to policymakers, healthcare providers and to the general public. The challenge is how to communicate rapidly changing data across langua…
2020-05-17 21:31:47+00:00 Read the full story…
Weighted Interest Score: 1.6653, Raw Interest Score: 1.1423,
Positive Sentiment: 0.1101, Negative Sentiment 0.3578

Speech Analytics Makes Unexpected Discovery

What the AI software bubbled up was the fact that the company was being inundated with calls from another part of their business that had to do with paper shredding. Why would they be getting calls for paper shredding, Chirokas wondered.

“The calls were coming from people who were concerned that they had to use their finger to sign the little handheld device to confirm that stuff was picked up,” he said. “Was that device being cleaned? How safe was that device? Am I going to get coronavirus from that device? “It was something that was completely unexpected and something they worked to resolve,” Chirokas added. “It was really kind of interesting.”

2020-05-13 00:00:00 Read the full story…
Weighted Interest Score: 1.1681, Raw Interest Score: 0.6397,
Positive Sentiment: 0.1706, Negative Sentiment 0.2701

Apple Hiring Focus: Cloud, Machine Learning Experts

A new report by Protocol suggests that Apple is hiring legions of cloud-computing experts, suggesting a rising interest in cloud-based apps and services.

“The quantity and quality of the new hires has caused a stir in the tight-knit cloud community,” the publication reported, “and could indicate that Apple is finally getting serious about building tech infrastructure on par with companies like Amazon, Microsoft and Google.” Many of these new employees have extensive experience working for Amazon Web Services (AWS) and Google.

2020-05-14 00:00:00 Read the full story…
Weighted Interest Score: 1.1027, Raw Interest Score: 0.8972,
Positive Sentiment: 0.1035, Negative Sentiment 0.1035

How To Leverage Your Website Data To Generate More Customers

Whenever people come to your website, you get more data about your audience. But are you actually leveraging that data to its full potential? What do you do his with all of this valuable information?

Your website’s traffic data is an extremely important resource for your business. Not only does it help you understand your audience better, as well as their behaviour, but when you fully leverage it, it can also help you get more customers and make more sales.

That’s why, in this blog post, we’re going to focus on how you can leverage your website data to generate more customers and grow your business in the process…
2020-05-15 19:46:40+00:00 Read the full story…
Weighted Interest Score: 1.0950, Raw Interest Score: 0.6067,
Positive Sentiment: 0.5179, Negative Sentiment 0.0740

5 Crazy And Powerful Data-Driven Internet Statistics In 2020

Big data is changing the future of the Internet. The World Wide Web existed long before “big data” became a household term. However, the two concepts have become virtually inseparable in recent years.

  1.  Around Four Billion People Are Active On The Internet Today
  2.  Most People Access The Internet Through A Mobile Device
  3.  There Are Over A Billion Active Websites
  4.  ECommerce Is A Billion-Dollar Industry
  5.  Social Media Accounts For A Large Percentage Of Internet Use

2020-05-12 16:43:32+00:00 Read the full story…
Weighted Interest Score: 1.0938, Raw Interest Score: 0.7549,
Positive Sentiment: 0.2157, Negative Sentiment 0.0924

Ascension’s Unconventional Approach to Healthtech

Instead of spending time shopping for solutions and helping startups better understand its health system, Ascension decided to cut out the middleman and launch its own in-house development shop, the Digital Studio. According to Ascension, the Digital Studio is designed to operate like a startup, with cross-functional teams located in St. Louis, Austin and Chicago building products in two-week sprints. But unlike a startup, the Digital Studio team doesn’t have to worry about running out of funding, finding product-market fit or getting their technology into the hands of doctors and nurses.

Caleb Dixon has worked in healthcare for more than 20 years, the last four of which have been spent with Ascension. Dixon, the Digital Studio’s operations lead, told Built In in an interview that Ascension’s approach to technology is uncommon in its industry. “We exist to change the way healthcare is delivered,” said Dixon.

2020-05-15 00:00:00 Read the full story…
Weighted Interest Score: 1.0108, Raw Interest Score: 0.8087,
Positive Sentiment: 0.2205, Negative Sentiment 0.0551


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