AI & Machine Learning News. 08, June 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?
Why TikTok made its user so obsessive? The AI Algorithm that got you hooked.
Tick Tok is taking the world by storm. According to Sensor Tower, the short video app has been downloaded more than 2 billion times globally on the App Store and Google Play. What’s the magic behind this sensational App that got you so obsessive? Not surprised, the answer is ML backed Recommendation Engine. Apart from the growth hacking strategy, this 60-second short video app is filled with memes, comedy, dancing, and talents. Equipped with one of the best Recommending Engine in the industry, You don’t need to search or know whom to watch. Personalized feed was provided at a click away.
Today, we are going to discuss how did TikTok use machine learning to analyze users’ interests and preferences through the interactions then display a personalized feed for different users. The recommendation engine is not new to the Data Science community. Instead, some consider it as the old generation AI system due to a lack of dizzying effects like image recognition or language generation.
2020-06-07 13:44:00.960000+00:00 Read the full story…
Weighted Interest Score: 2.5344, Raw Interest Score: 1.1716,
Positive Sentiment: 0.1400, Negative Sentiment 0.1400
CloudQuant Thoughts : The top 4 accounts on Tik Tok have a combined follower total of 190m yet, unless you are under 20, you probably do not know any of them (@charlidamelio 61.5, @zachking 44.4, @addisonre 44.2, @lorengray 43.9). This Chinese company is shaping the thoughts of our youth in 15 second bytes. Their AI algorithm just works. They have been researching presenting news and can read 5000 news sources and create a personlized news article for any user in 2 seconds. On Tik Tok you don’t choose, you just like or dislike and the feed keeps coming.
South Korea unveils US$62B ‘New Deal’ to reshape post-virus economy
The South Korean government unveiled a 76 trillion won (US$62 billion) ‘New Deal’ spending plan to reshape the economy in the aftermath of the pandemic after slashing its growth forecast for the year.
The plan, first outlined by Moon in April, aims to refocus the economy through 2025 by supporting job growth and new industries. It will partly be funded by a third extra budget now being drafted, according to a statement on the policy outlook for the second half.
The focus is to promote the use of fifth generation wireless networks and artificial intelligence across industries and foster digitalization in South Korea’s least developed areas. Investment will also support startups focusing on green technologies, while the country seeks to make its manufacturing sector more energy-efficient.
2020-05-28 Read the full story…
CloudQuant Thoughts : As we (hopefully) come out of this Covid lockdown with the damage it has done to our economies, this is what we should be doing. We should be making major steps forwards in technology, aligning our spending with a better future and positioning ourselves at the very front of AI and ML.
Widespread face mask use could make facial recognition less accurate
Amazon’s widely sold facial recognition technology is ‘robust’ enough to counter face masks, but Apple’s technology falls short of the mark
As more people began to wear masks in public to help prevent the spread of coronavirus, iPhone owners quickly spotted a problem. With people’s noses and mouths covered, the phone’s facial recognition system used to unlock its devices stopped functioning, frustrating users.
Apple issued a quick fix. Now iPhones instantly recognise if a person is wearing a mask, and asks them to enter a passcode, instead of attempting the facial recognition system. But the original inconvenience demonstrated a greater problem.
Like Apple’s Face ID, which requires eyes, nose and mouth to be visible in order for it to work, many algorithms that are trained on publicly available datasets tend to focus on all of our facial features, typically with emphasis on the lower region around the lips. It is these algorithms, largely used by smartphones, that will continue to fail while people are wearing face masks, experts say.
2020-06-04 00:00:00 Read the full story…
Weighted Interest Score: 2.5113, Raw Interest Score: 1.0169,
Positive Sentiment: 0.1977, Negative Sentiment 0.3107
CloudQuant Thoughts : Chinese facial recognition is already working through masks and with this recent Deep Learning white paper claiming to be able to accurately reproduce a face from an image of that persons ear, ML and AI appear to be leapfrogging the problem.
Automation, Algos, AI Rebound in Fixed Income Trading
One step back, two steps forward.
That was the path for automation, algorithms and artificial intelligence applications in fixed income trading over the past few months.
Extreme volatility in March amid the unprecedented COVID-19 pandemic prompted some traders to back off newer, less proven trading technologies in favor of old-fashioned telephone transactions. But as vol subsided, automation, algos and AI reclaimed a front burner — and market participants and observers say the tumult made the technologies stronger ahead of the next disruption.
2020-06-04 19:23:11+00:00 Read the full story…
Weighted Interest Score: 6.3033, Raw Interest Score: 2.1317,
Positive Sentiment: 0.1895, Negative Sentiment 0.1421
CloudQuant Thoughts : The only learning I can see coming out of the Bond markets and Fixed Income Trading is that one should watch trading volume over volatility. Write yourself a FED detection routine so you know when the FED is stepping in.
ARK Invest: AI training costs dropped 100-fold between 2017 and 2019
Machine learning systems are cheaper to train now than ever before. That’s the assertion of ARK Invest, which today published a meta-analysis indicating the cost of training is improving at 50 times the pace of Moore’s law, the principle that computer hardware performance doubles every two years.
In its report, ARK found that while computing devoted to training doubled in alignment with Moore’s law from 1960 to 2010, training compute complexity — the amount of petaflops (quadrillions of operations per second) per day — increased by 10 times yearly since 2010. Coinciding with this, training costs over the past three years declined by 10 times yearly; in 2017, the cost to train an image classifier like ResNet-50 on a public cloud was around $1,000, while in 2019, it was around $10.
2020-06-04 00:00:00 Read the full story…
Weighted Interest Score: 2.4365, Raw Interest Score: 1.2768,
Positive Sentiment: 0.2043, Negative Sentiment 0.2298
CloudQuant Thoughts : This is both staggering and totally expected. And as AI improves and begins to design both its own software and hardware, we will see massive steps forward in performance and cost (both $ and energy).
Arduous Hurdles To Overcome In Scaling Up Of AI Autonomous Cars
Scaling up is abuzz.
What does it mean to seek or reach scale? Generally, for start-ups, the notion is that you sometimes start relatively small, perhaps making a prototype or a minimally viable product (MVP), and show it off to gain attention and funding. Potential investors and actual investors are usually of the belief that the one-time version of your product can become a mass-produced one. This is not always the case or might be exorbitantly costly to achieve. Something that you might have hand-crafted could be terribly difficult and expensive to recreate and produce on any sizable volume. Furthermore, your product might work for a handful of situations that you tested, but once it is put into wider use, you could unexpectedly discover that it has limitations or flaws of a fatal kind or that constrain your market potential for the product.
Here’s then the question for the day: Will AI-based self-driving driverless autonomous cars be able to scale? Many outside the driverless car industry are assuming that if you can make one self-driving car, you can make zillions of them. This assumption is not necessarily the case.
2020-06-04 21:30:06+00:00 Read the full story…
Weighted Interest Score: 2.3975, Raw Interest Score: 0.8817,
Positive Sentiment: 0.1126, Negative Sentiment 0.2189
Why the buzz around DeepMind is dissipating as it transitions from games to science
DeepMind shot to fame in 2016 when it built a computer program called AlphaGo that learned how to play the board game Go and became better than any human. The London AI lab, which is owned by Alphabet, is now going through a quieter period, with far less media attention. DeepMind is shifting its focus from building “AI agents” that can play games to building AI agents that can have real world impact, particularly in areas of science like biology.
DeepMind’s army of 1,000 plus people, which includes hundreds of highly-paid PhD graduates, continues to pump out academic paper after academic paper, but only a smattering of the work gets picked up by the mainstream media. The research lab has churned out over 1,000 papers and 13 of them have been published by Nature or Science, which are widely seen as the world’s most prestigious academic journals. Nick Bostrom, the author of Superintelligence and the director of the University of Oxford’s Future of Humanity Institute described DeepMind’s team as world-class, large, and diverse.
2020-06-05 00:00:00 Read the full story…
Weighted Interest Score: 2.0816, Raw Interest Score: 0.9776,
Positive Sentiment: 0.2834, Negative Sentiment 0.1700
How I passed the TensorFlow Developer Certification Exam – And how you can too
Curriculum — what I studied to build the skills necessary for passing the exam
It should be noted that before I started studying for the exam, I had some hands-on experience building several projects with TensorFlow.
The experienced TensorFlow and deep learning practitioner will likely find they can go through the following curriculum at about the same pace I did (3 weeks total), maybe faster.
2020-06-07 02:37:34.940000+00:00 Read the full story…
Weighted Interest Score: 2.2735, Raw Interest Score: 1.2747,
Positive Sentiment: 0.0000, Negative Sentiment 0.0524
The Essential Guide to Training Data (PDF Behind Registration Wall)
There’s a saying: “Garbage in, garbage out.” It’s common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms — the training data itself. Your model is only as good as the data it’s trained on. The Essential Guide to Training Data covers everything you need to know about creating the training data necessary to drive successful machine learning projects.
2020-06-05 00:00:00 Read the full story…
Weighted Interest Score: 5.1836, Raw Interest Score: 1.9608,
Positive Sentiment: 0.6536, Negative Sentiment 0.0000
Nasdaq Invests in Automated Financial Crime Investigator
Nasdaq Ventures Invests in Automated Financial Crime Investigations Firm Caspian
“Caspian’s proven solution solves a huge pain point in the industry, dramatically increasing analyst productivity and resulting in meaningful cost-savings for bank compliance teams,” said Chris Brannigan, CEO, Caspian, “Our machine learning technology is validated through production use at global financial institutions, making risk decisions that are fully explainable and regulator friendly. Through the investment and partnership with Nasdaq we are excited to expand our offering at a global scale.”
2020-06-04 13:14:13+00:00 Read the full story…
Weighted Interest Score: 4.7683, Raw Interest Score: 2.0688,
Positive Sentiment: 0.6179, Negative Sentiment 0.5373
How good are the pollsters? Analyzing Five-Thirty-Eight’s dataset
We analyze the pollster ranking dataset from the venerable political prediction website Five-Thirty-Eight.
This is an Election Year and polling scene around the elections (both General Presidential and House/Senate) is heating up. This will become more and more exciting in the coming days, with tweets, counter-t…
2020-06-07 19:26:49.741000+00:00 Read the full story…
Weighted Interest Score: 1.9532, Raw Interest Score: 0.7894,
Positive Sentiment: 0.1619, Negative Sentiment 0.1316
Artificial Intelligence Is Dumb Without The Elasticity Quotient
How much has your business really changed since implementing AI? I’m not asking about lift for discrete metrics such as customer loyalty, campaign revenue, or even process optimization. Was the DNA of your enterprise altered by tweaking the edges? Of course not, because the elasticity quotient was not met.
The elasticity quotient links the KPIs of the business with economic and market behaviors, letting enterprises expand, contract, and adapt at will for seen and unforeseen events.
Forrester’s analysis of more than 100 case studies provided by software vendors and service providers shows a disconnect. AI leads to measurable improvement in process outcomes, but less than 10% of firms demonstrated AI implementations affecting overall business revenue, profitability, and shareholder value. In one case, the board of directors of a transportation company asked why they should be investing more in AI after three years with no significant overall return.
2020-06-02 19:04:55-04:00 Read the full story…
Weighted Interest Score: 4.6183, Raw Interest Score: 1.7262,
Positive Sentiment: 0.2877, Negative Sentiment 0.2877
5 Essential Papers on AI Training Data
Many data scientists claim that around 80% of their time is spent on data preprocessing, and for good reasons, as collecting, annotating, and formatting data are crucial tasks in machine learning. This article will help you understand the importance of these tasks, as well as learn methods and tips from other researchers.
Below, we will highlight academic papers from reputable universities and research teams on various training data topics. The topics include the importance of human annotators, how to create large datasets in a relatively short time, ways to securely handle training data that may include private information, and more.
2020-06-05 00:00:00 Read the full story…
Weighted Interest Score: 4.1553, Raw Interest Score: 2.1545,
Positive Sentiment: 0.2428, Negative Sentiment 0.1365
Essential Artificial Intelligence Trends That Can Shape The Future
These important artificial intelligence trends can help shape the future, and they can play a key role in planning for 2020 and beyond.
It goes without saying that everybody is interested in adopting Artificial Intelligence and that too on a larger scale. However, based on global surveys, almost 70 percent of newer startups aren’t using a lot of AI tools with their absence having minimal impact on the growth of the concerned organizations. That said, while some aren’t sure about the effectiveness of this technology, we feel that this untapped resource isn’t being utilized in a desirable manner. This is why we thought of sharing our insights regarding the latest and upcoming AI trends that could be strategic inclusions, especially in the next few years to come.
2020-06-05 14:57:36+00:00 Read the full story…
Weighted Interest Score: 4.0101, Raw Interest Score: 1.5310,
Positive Sentiment: 0.3682, Negative Sentiment 0.2907
OpenAI and Dota 2 Change the Face of Gaming Forever
Elon Musk has unveiled OpenAI, a new AI project that is disrupting the gaming industry. How will it all play out?
We learn from our mistakes, and artificial intelligence is the same. AI learns best when it is beaten. Elon Musk has founded an AI lab that has developed a squad of AI bots to compete in the game of Dota 2 and some other games as well.
Video games lack the intellectual reputation of chess and Go, but they are much harder for computers to compete in. Video games are more complex, and ever-changing, especially MOBAs like Dota 2. At first “OpenAI five” (as the AI team is called) beat the current reigning champions 2-0…
2020-06-01 18:01:00+00:00 Read the full story…
Weighted Interest Score: 3.8731, Raw Interest Score: 1.0930,
Positive Sentiment: 0.5886, Negative Sentiment 0.2803
Why Crypto needs robo-advisors?
Robotization coupled with artificial intelligence (AI) are reshaping every aspect of our lives. When we hear the term “robo-advisor”, our imagination is filled with images from science-fiction movies. But when we refer to robo-advisors, we mean things like bots, virtual robots or algorithms that automate different tasks. Robo-advisors grew out of the ashes of the 2008 financial crisis. Robo-advisors gained traction when people lost faith in trad…
2020-06-01 00:00:00 Read the full story (subscription required)…
Weighted Interest Score: 3.8158, Raw Interest Score: 1.3827,
Positive Sentiment: 0.1796, Negative Sentiment 0.1437
BNP Paribas Securities Services uses NLG to write client exec summaries
BNP Paribas Securities Services is using Natural Language Generation (NLG) to write one-page executive summaries for its custody clients.
The bank says NLG allows it to transform large amounts of structured global custody data into “concise and insightful commentaries”. Clients will receive their traditional monthly statistics reports providing in-depth data on their operations but will now also get the brief summary.
This will alert them to unusual patterns and highlight areas for improvement and best practices, boosting oversight, controls and efficiency, says BNP. For example, the summary points to the percentage of corporate actions instructions received after deadline or to manual instructions rates and suggests specific actions.
2020-06-05 14:52:00 Read the full story…
Weighted Interest Score: 3.7827, Raw Interest Score: 2.5292,
Positive Sentiment: 0.4864, Negative Sentiment 0.0000
What Are The Pros And Cons Of Artificial Intelligence?
The pros and cons of artificial intelligence are important to consider as the technology grows. Here’s what to know about it.
Artificial intelligence (AI) is a hot topic these days, but it’s not a perfect technology. AI is like almost anything else in that it has both advantages and downsides. What are the pros and cons of artificial intelligence? Here’s what people bring up most often.
- It Boosts Efficiency
- It Improves Forecasting
- It Enhances Quality Control
- It Makes Humans Too Trusting in Technology
- It Shows Biases
- It Lacks Universal Ethical Standards
2020-06-08 10:30:00+00:00 Read the full story…
Weighted Interest Score: 3.5066, Raw Interest Score: 1.2459,
Positive Sentiment: 0.3250, Negative Sentiment 0.2979
The AI community says Black Lives Matter, but more work needs to be done
This week, as thousands of protestors marched in cities around the U.S. to bring attention to the death of George Floyd, police brutality, and abuses at the highest levels of government, members of the AI research community made their own small gestures of support. NeurIPS, one of the world’s largest AI and machine learning conferences, extended its technical paper submission deadline by 48 hours. And researchers pledged to match donations to Black in AI, a nonprofit promoting the sharing ideas, collaborations, and discussion of initiatives to increase the presence of black people in the field of AI.
“NeurIPS grieves for its Black community members devastated by the cycle of police and vigilante violence. [We] mourn … for George Floyd, Breonna Taylor, Ahmaud Arbery, Regis Korchinski-Paquet, and thousands of black people who have lost their lives to this violence. [And we stand] with its black community to affirm that, today and every day, black lives matter,” the NeurIPS board wrote in a statement announcing its decision.
2020-06-05 00:00:00 Read the full story…
Weighted Interest Score: 3.3767, Raw Interest Score: 1.3263,
Positive Sentiment: 0.2002, Negative Sentiment 0.3504
The Logical Data Fabric: A Single Place for Data Integration
The ability to provide a single place for instantaneous data access can mean business continuity or closure. Many nations found this out during the recent global crisis, as countries needed to know the number of tests taken and the infection rate in order to determine both the virus’ spread and who to quarantine. Unfortunately, the required data did not become available in time to prevent a widespread lockdown, shuttering many businesses. Simultaneously, demands for immediate integrated critical data only increased.
Getting high-quality data now, for discovery and solutions, is a top priortiy for Ravi Shankar, the Senior Vice President and Chief Marketing Officer for Denodo. In a recent DATAVERSITY® interview, Shankar explained how data virtualization technologies are evolving and creating a logical data fabric, putting data all into one place and enabling better and faster business decisions.
2020-06-04 07:35:27+00:00 Read the full story…
Weighted Interest Score: 3.3695, Raw Interest Score: 1.8913,
Positive Sentiment: 0.2113, Negative Sentiment 0.1308
DeepMind Releases Acme – A Framework To Decrease Complexities In AI Workflows
DeepMind, on 1st June, released Acme — a framework for building reliable, efficient, research-oriented RL algorithms. According to the researchers, the idea behind building the Acme framework was to decrease complexities in ML-based solutions, as well as help researchers and firms, to scale effortlessly.
While we have witnessed major advancements in deep learning and computational power, complexities in developing robust solutions have also increased rapidly. Such challenges, according to the authors of the paper, has increased the difficulties for researchers to rapidly prototype ideas, thereby causing serious reproducibility issues.
Reproducibility has brought numerous criticism to the AI-based models as it has decreased trust among the users. However, with Acme, the researchers of DeepMind believe that the framework will mitigate the challenges of reproducibility and simplify the process for researchers to develop novel and creative algorithms. With Acme, one will able to scale while ensuring RL agents deliver desired results.
2020-06-04 14:14:01+00:00 Read the full story…
Weighted Interest Score: 3.2588, Raw Interest Score: 1.3514,
Positive Sentiment: 0.4660, Negative Sentiment 0.2330
Citi just launched a new team that will apply data science to dealmaking. It shows how much tech is changing investment banking.
Citigroup is forming a new investment banking team, combining the firm’s activism and shareholder defense, data science, and corporate finance advisory groups. The group aims to infuse data science into specialized teams to bolster dealmaking advice and make it more accessible across the division. The new global unit, called strategic advisory solutions, will number more than 80 bankers and will be led by top activism defense banker Muir Paterson.
2020-06-05 00:00:00 Read the full story…
Weighted Interest Score: 3.0600, Raw Interest Score: 1.6235,
Positive Sentiment: 0.1635, Negative Sentiment 0.0934
Now’s the time to look at best practice around risk modelling
Using advanced mathematical modelling for calculating, predicting and evaluating risk is nothing new. Financial institutions of all kinds have long been using numerical libraries, whether home-grown or from a third party, containing mathematical, statistical and — increasingly — machine learning algorithms. However, the increasing complexity, market evolution, economic challenges and sheer scale of the data involved all mean that risk modelling is more imperative yet more challenging than ever before. Now may be the ideal time to evaluate and even re-think the processes and tools being used for risk analysis.
Given the potential impact on a business, everyone within a financial services firm benefits from having some understanding of the best practice strategy around risk analysis: get it wrong and the organisation is vulnerable; get it right, and it can be more confident in their decision-making and forward-planning.
2020-06-08 13:51:27 Read the full story…
Weighted Interest Score: 3.0355, Raw Interest Score: 1.6721,
Positive Sentiment: 0.2306, Negative Sentiment 0.2306
Making the Most of Your Investment in Hadoop (White Paper behind Registration Wall)
Hadoop is a popular enabler for big data. But with data volumes growing exponentially, analytics have become restricted and painfully slow, requiring arduous data preparation. Often, querying weeks, months, or years of data is simply infeasible, and organizations succeed in analyzing only a fraction of their data.
The now expensive nodes you need to support are strained, and the complex data architecture built around Hadoop struggles to bring business insights.
Download the whitepaper to learn:
- How to deal with the exponential growth of data
- How to reduce time spent on data preparation
- How to generate insights faster from ad-hoc queries of raw data
2020-06-02 00:00:00 Read the full story…
Weighted Interest Score: 2.9940, Raw Interest Score: 1.7964,
Positive Sentiment: 0.3992, Negative Sentiment 0.3992
What Is a Data Cloud? And 11 Other Snowflake Enhancements
Snowflake has changed how the industry thinks about data warehouses with its cloud-native offering, which has been adopted by 4,000 organizations, including 2,000 in the last year alone. Now the company is taking the concept one step further with the introduction of a data cloud, which the company is positioning as a one-stop shop where organizations can execute a full range of data-oriented tasks – not just data warehousing and SQL analytics, but also machine learning, data engineering, and monetization of third-party data.
According to Snowflake CEO Frank Slootman, the origins of the data cloud concept began with the rise of public clouds from Amazon Web Services, Microsoft Azure, and Google Cloud, which Snowflake sits atop. Simultaneously, the rise of software as a service (SaaS) applications, or “application clouds,” from the likes of Salesforce, Workday, and SAP, have provided transactional data to process on the clouds.
But getting the transactional data into the public cloud so they can work together in a harmonious manner is much easier said than done, Slootman says.
2020-06-02 00:00:00 Read the full story…
Weighted Interest Score: 2.7260, Raw Interest Score: 1.6381,
Positive Sentiment: 0.2694, Negative Sentiment 0.1293
Discover the Power of DataOps
A new methodology is on the rise at insights-hungry enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation, to reporting.
However, improving the flow of data between managers and consumers within an organization through greater communication, integration and automation is no simple task, and it requires cultural changes as well as enabling technologies. DBTA recently held a roundtable webinar featuring Dan Potter, VP of product marketing, Qlik; Douglas McDowell, chief strategy officer, SentryOne; and Chris Bergh, CEO and head chef, DataKitchen, who discussed key success factors and emerging best practices in the DataOps space.
2020-06-03 00:00:00 Read the full story…
Weighted Interest Score: 2.7027, Raw Interest Score: 1.4742,
Positive Sentiment: 0.5265, Negative Sentiment 0.1053
What is machine learning, and how does it work? (Video Explainer 4m)
At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.
In a digital world full of ever-expanding datasets like these, it’s not always possible for humans to analyze such vast troves of information themselves. That’s why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.
2020-06-04 00:00:00 Read the full story…
Weighted Interest Score: 2.6852, Raw Interest Score: 1.5858,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000
Expanding Your Data Science and Machine Learning Capabilities (Webinar Registration)
Expanding Your Data Science and Machine Learning Capabilities
SPECIAL DBTA ROUNDTABLE WEBINAR THURSDAY, JUNE 25, 2020 – 11:00 am PT / 2:00 pm ET
Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. In responses, new solutions have emerged to deliver key capabilities in areas including visualization, self-service and real-time analytics. Along with the rise of DataOps, greater collaboration and automation have been identified as key success factors.
2020-06-25 00:00:00 Read the full story…
Weighted Interest Score: 2.5744, Raw Interest Score: 1.7004,
Positive Sentiment: 0.2429, Negative Sentiment 0.0810
20 Developer Roles with Notably High Salaries
Which types of developer roles result in the biggest salaries? That’s a vital question for developers who are plotting out their career trajectory, and questioning whether they should aim for a management role at some point.
This year’s Stack Overflow Developer Survey is a good place to start. Based on 8,006 responses, the survey’s data shows that, at least in the United States, engineering managers make the most money (at an average of $152,000 per year) followed by specialists in various development categories such as data science, machine learning, and DevOps. Check out the chart:
2020-06-08 00:00:00 Read the full story…
Weighted Interest Score: 2.4937, Raw Interest Score: 1.8644,
Positive Sentiment: 0.1271, Negative Sentiment 0.1695
Semiconductor Miniaturisation Is Running Out Of Steam. Time To Focus On Smarter Algorithms
Recently, a team of researchers from MIT CSAIL recommended that researchers should focus on three key areas that prioritise to deliver computing speed-ups, which are new algorithms, higher-performance software and more specialised hardware, and the need for moving away from focusing on creating only smaller hardware.
The researchers stated that semiconductor miniaturisation is running out of steam as a viable way to grow computer performance, and industries will soon face challenges in their productivity. However, the opportunities for growth in computing performance will still be available if the researchers focus more on software, algorithms, including hardware architecture.
2020-06-07 02:30:00+00:00 Read the full story…
Weighted Interest Score: 2.4562, Raw Interest Score: 1.4319,
Positive Sentiment: 0.3420, Negative Sentiment 0.1496
ZoomInfo raises $887M in blockbuster IPO, rings virtual Nasdaq bell
Another company is zooming onto Wall Street, signaling that the window for initial public offerings is opening up even amid a fragile period for the global economy.
ZoomInfo Technologies, a Vancouver, Wash.-based software company that uses machine learning to help more than 15,000 customers drive sales and marketing programs, raised $887 million as the 13-year-old company sold shares at $21 per share. The 1,287-person company — led by co-founder and CEO Henry Schuck and originally known as DiscoverOrg — is starting trading today on Nasdaq under the ticker ZI.
2020-06-04 15:21:00+00:00 Read the full story…
Weighted Interest Score: 2.4374, Raw Interest Score: 1.2696,
Positive Sentiment: 0.1336, Negative Sentiment 0.2339
What is the Difference Between CNN and RNN?
Convolutional Neural Networks and Recurrent Neural Networks are commonly used in ML today. However, they are often used for completely different use cases.
In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.
2020-06-08 04:00:37.540000+00:00 Read the full story…
Weighted Interest Score: 2.3790, Raw Interest Score: 1.3259,
Positive Sentiment: 0.1535, Negative Sentiment 0.0977
The Long (and Artificial) Arm of the Law: How AI is Used in Law Enforcement
Artificial intelligence. Nowadays, it seems like it’s everywhere. From the computers we use at work, to the cars we drive, to the self-checkout stations and ATMs we use practically every day.
Now, not only do we speak to, and through, our technology, but our technology is also speaking back. It helps us in our banking, our healthcare, our entertainment, and beyond.
But today, new uses are being found for artificial intelligence (AI), uses designed to keep us safe and well. In fact, it may well turn out that what AI is actually best at is not keeping us productive or occupied. It may be that what AI is best at is keeping us, as well as our men and women in blue, alive. For example, AI might be used to help police officers identify high-risk areas, individuals, or situations, using applications already proving highly effective in the healthcare industry.
2020-06-03 00:00:00 Read the full story…
Weighted Interest Score: 2.2651, Raw Interest Score: 0.9582,
Positive Sentiment: 0.1825, Negative Sentiment 0.4563
BMC Unveils AIOps for the Modern Mainframe
BMC, which just completed the purchase of Compuware, has unveiled BMC AMI Operational Insight, an AI-driven solution that uses machine learning to detect anomalies and maximize lead time for remediation to mitigate mainframe issues before they become business problems.
BMC offers a full suite of mainframe software development, delivery, and performance solutions that empower organizations to scale Agile and DevOps with a fully integrated toolchain.
2020-06-15 00:00:00 Read the full story…
Weighted Interest Score: 2.1748, Raw Interest Score: 1.6336,
Positive Sentiment: 0.3112, Negative Sentiment 0.5834
Trends In Business Intelligence And Data Science For Retail
When it comes to retail, it’s important to watch for trends in business intelligence and data science.
Succeeding in retail isn’t easy. Shifting customer tastes, shrinkage risks and reduced traffic at physical stores are just some of the challenges retailers may face as they try to operate profitably. Many are relying on data science and business intelligence (BI) tools to get ahead of competitors and stay resilient in a challenging marketplace.
Here are five trends in business intelligence and data science for retail worth knowing:
2020-06-05 09:30:00+00:00 Read the full story…
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