AI & Machine Learning News. 02, March 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?
How Data and Technology are Changing Active Portfolio Management
We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream.
However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies.
I will touch upon some key themes in this blog, but will go into more detail in a series to follow.
- Quantamental investing
- Data science and AI
- Sustainable investing
2020-02-28 15:53:44+00:00 Read the full story…
Weighted Interest Score: 6.0569, Raw Interest Score: 2.3309,
Positive Sentiment: 0.1650, Negative Sentiment 0.1856
CloudQuant Thoughts : The combination of fundamental and Quant techniques, the application of Data Science and AI, the importance of finding and retaining talent have long been important factors in Data Science but the Sustainable Investing is the new Data Point in town. CloudQuant have an ESG dataset available via our Data Catalog. We are constantly looking for new data sets, we test them and produce our own White Papers with reproducible results using our own Backtesting system.
Women in Data Science (WiDS) Conference Livestream – March 2nd
The Women in Data Science (WiDS) initiative aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field.
WiDS started as a conference at Stanford in November 2015. Now, WiDS includes a global conference, with approximately 150+ regional events worldwide; a datathon, encouraging participants to hone their skills using a social impact challenge; and a podcast, featuring leaders in the field talking about their work, their journeys, and lessons learned.
2020-03-02 00:00:00 Read the full story…
Weighted Interest Score: 2.9308, Raw Interest Score: 1.8171,
Positive Sentiment: 0.4103, Negative Sentiment 0.2345
CloudQuant Thoughts : Here at CloudQuant we are happy to promote WiDS and to encourage increased diversity in Data Science.
AI Algorithm Improves Crop Yield Prediction
As climate change puts greater and greater stressors on crops, precision agriculture – which pursues lower inputs and higher yields – is a booming market, poised to reach nearly $13 billion by the late 2020s. Technology improvements are at the core of many of the solutions that guide the search for climate-resilient and precision agriculture, and now, a team from the University of Illinois has created a new AI algorithm that can use data to help guide crop management decisions in real-time.
A few years ago, the university undertook an effort called the Data-Intensive Farm Management Project. Through this project, researchers seeded and fertilized over 200 fields in the U.S., South America, and even South Africa at varying rates. These researchers used that data – specifically, data from a subset of Midwestern corn fields – as their starting point. They broke the fields down into 5-meter squares and fed data from the project (including elevation, nitrogen application, seed rate, and more) into a convolutional neural network (CNN). Then, they tasked the neural network with predicting yield.
2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 4.3618, Raw Interest Score: 1.7564,
Positive Sentiment: 0.1756, Negative Sentiment 0.0585
CloudQuant Thoughts : As AI and ML functionality gets easier to use, more and more industries will be revolutionized by their discoveries. The idea of monitoring entire farms in 5m x 5m squares (for multiple data points) was pure science fiction just a few years ago, let alone feeding the information into a convolutional neural network to make sense of the data.
Financial AI Is the Missing Key to Ending Human Trafficking
Technology has opened up a world of possibility, for good and for bad. Some enable criminals to operate on a level previously unseen, but the solution to stopping them often also lies in tech. At today’s rapid pace of development, catching the bad guys is usually a matter of having the most advanced tools.
Some of the most pressing criminal concerns have moved to the digital sphere. As a result, cyber justice can take the form of a technological arms race. Clever implementation of technology may have allowed criminals to avoid capture in the first place, but as security tech improves, it may prove to be their downfall.
Human trafficking is one crime that has proved historically challenging to address. Those guilty of this heinous activity have repeatedly slipped the grasp of law enforcement, but thanks to new tools like artificial intelligence (AI), that may be changing.
2020-02-27 22:30:16+00:00 Read the full story…
Weighted Interest Score: 2.7935, Raw Interest Score: 1.2319,
Positive Sentiment: 0.3715, Negative Sentiment 0.6648
CloudQuant Thoughts : These are the AI and ML stories that fill me with hope for the future. Human Traffickers have been utilizing new technology to expand their scope so it is great to see that AI is being used to close the net on these offenders.
AI Put to Work to Help US Steel Industry Stay Competitive
As the US steel industry looks for ways to lower costs in a global market facing slowing demand, a modern steel plant in Arkansas is using AI to help it become more competitive.
The Big River Steel Mill, which began operating in January 2017, melts scrap metal and produces steel for more than 200 customers, including four automakers, according to a recent account in WSJPro.
The plant’s AI system has been designed by Noodle Analytics of San Francisco, which uses deep learning and neural networks to continually train algorithms on data captured by thousands of sensors.
“We’re using the best available technology and pressing that technology farther, we think, than anyone in the steel industry,” stated Big River Chief Executive David Stickler, a veteran of the steel, mining and recycling industries. “Any future steel facilities that are built will try to capitalize on what we’ve done and replicate it.”
2020-02-27 22:30:23+00:00 Read the full story…
Weighted Interest Score: 2.7727, Raw Interest Score: 1.3185,
Positive Sentiment: 0.2728, Negative Sentiment 0.1591
CloudQuant Thoughts : If we move fast enough we can reduce our costs of production to lower than the current cost of Chinese labor. Adidas tried to move production of Sneakers back to Germany and the US but will be closing its SpeedFactory sites later this year and moving some of the technology to Asia. The shortages of medical supplies for treatment of the CoronaVirus as a result of the over reliance on China for production demonstrates how important it is for us to do better!
IEX’s plan to ‘Thwart predatory trading with AI’ gets pension backing
A group of North American retirement plans with more than $3.3 trillion in assets has backed a proposal by exchange operator IEX Group to use artificial intelligence to counter technology some high-speed traders use to get a trading edge. New technologies and regulations have made the U.S. equity market more efficient. But they have also created speed advantages when executing stock orders, the group, led by pension plans in Ontario and Quebec, as well as the New York City Comptroller, said in a letter to the U.S. Securities and Exchange Commission.
“These speed advantages have tilted the playing field in favor of firms specializing in ‘latency arbitrage,’ reducing the willingness of both long-term investors and market makers to display quotes,” the group, which also includes retirement plans in California, Wyoming, and Arizona, said in the Feb. 24 letter. In latency arbitrage, when a stock price changes on one of the 13 U.S. exchanges, a firm uses advanced technology to race ahead electronically to the other exchanges microseconds before the price updates to buy or sell at an advantageous level.
2020-02-25 20:11:46+00:00 Read the full story…
Weighted Interest Score: 3.2593, Raw Interest Score: 1.3463,
Positive Sentiment: 0.2244, Negative Sentiment 0.1496
Top Google AI Tools for Everyone
“We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors.” — Jeff Dean, Google Senior Fellow
Calling Google just a search giant would be an understatement with how quickly it grew from a mere search engine to a driving force behind innovations in several key IT sectors. Over the past couple of years, Google has planted its roots into almost everything digital, be it consumer electronics such as smartphones, tablets, laptops, its underlying software such as Android and Chrome OS or the smart software backed by Google’s AI.
Google has been actively innovating in the smart software industry. Backed by its expertise in search and analytical data acquired over the years have helped Google create various tools like TensorFlow, ML Kit, Cloud AI and many more for enthusiasts and beginners alike, trying to understand the capabilities of AI. Google AI is focused on bringing the benefits of AI to everyone. The following sections will shed more light on how Google has targeted its suite of tools to specific groups of users, such as Developers, Researchers and Organizations and how they can benefit from the AI tools by Google.
2020-02-29 14:43:52.706000+00:00 Read the full story…
Weighted Interest Score: 3.5372, Raw Interest Score: 1.9825,
Positive Sentiment: 0.3130, Negative Sentiment 0.1252
Rethinking how we value data – Looking at the world’s most precious resource through new eyes
Everyone knows that data are worth something. The biggest companies in the world base their businesses on them. Artificial-intelligence algorithms guzzle them in droves. But data are not like normal traded goods and services, such as apples and haircuts. They can be used time and again, like public goods. They also have spillover effects, both positive, such as helping to improve health care, and negative, such as breaches of personal information. That makes them far from easy to value.
A new report, led by Diane Coyle, an economist at the University of Cambridge, attempts to address this by understanding the value of data and who stands to benefit from it. She says market prices often do not ascribe full value to data because, in many cases, trading is too thin. Moreover, while much of society’s emphasis is on the dangers of misuse of personal data, the report chooses to highlight data’s contribution to “the broad economic well-being of all of society.” That gives it a much deeper value than a simple monetary one.
2020-02-27 00:00:00 Read the full story (Paywall)…
Weighted Interest Score: 4.9312, Raw Interest Score: 1.8729,
Positive Sentiment: 0.1422, Negative Sentiment 0.1660
Pope weighs in on AI ethics debate
The Pontiff is the latest public figure to offer an opinion on the ethics of using artificial intelligence (AI), issuing a set of principles on the use of new technology. The Vatican has produced the Rome Call for AI Ethics, which calls for AI technology to respect privacy, work reliably and without bias, operate transparently and “consider the needs of all human beings”.
Tech giants Microsoft and IBM have been recruited to act as technology sponsors for a project that apparently grew out of concerns raised by Pope Francis more than a year ago about the societal impact of AI. “His major concerns were, will it be available to everyone, or is it going to further bifurcate the haves and the have-not’s?” said John Kelly II, executive vice president of IBM and one of the signatories for the document, in comments reported by Reuters.
2020-02-28 11:18:00 Read the full story…
Weighted Interest Score: 3.2947, Raw Interest Score: 1.2821,
Positive Sentiment: 0.1832, Negative Sentiment 0.3663
Rome Call For AI Ethics: A Humanising Pledge Signed By The Tech Giants
Artificial intelligence (AI) has been seen as a transformative tech, where AI is now used to do significant functionalities of our lives. AI has enabled companies and governments to keep a constant tab on what humans are doing, therefore several questions have been raised, by critics, about its privacy and ethics. To promote ethical use of artificial intelligence (AI), for protecting the planet and the rights of the peo…
2020-03-02 09:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6587, Raw Interest Score: 1.2859,
Positive Sentiment: 0.1944, Negative Sentiment 0.2093
Healthcare Providers Beginning to Apply AI More in Patient Care
Hospitals and doctors’ offices collect vast amounts of data on their patients, everything from blood pressure to genetic sequencing. While the data may be digitized, using it to help in patient treatment can be challenging. But the healthcare industry is getting better at using AI to find patterns in data that can help in patient care.
“I think the average patient or future patient is already being touched by AI in health care. They’re just not necessarily aware of it,” stated Chris Coburn, chief innovation officer for Partners HealthCare System, a hospital and physicians network based in Boston, in an account in WebMD. The application of AI to patient care is in an early stage and is spreading.
“I could not easily name a [health] field that doesn’t have some active work as it relates to AI,” stated Coburn, who mentioned pathology, radiology, spinal surgery, cardiac surgery, and dermatology as examples.
2020-02-27 22:30:03+00:00 Read the full story…
Weighted Interest Score: 4.6781, Raw Interest Score: 1.5464,
Positive Sentiment: 0.3651, Negative Sentiment 0.1503
DOD Adopts Ethical Principles for Artificial Intelligence
The U.S. Department of Defense officially adopted a series of ethical principles for the use of Artificial Intelligence today following recommendations provided to Secretary of Defense Dr. Mark T. Esper by the Defense Innovation Board last October.
The recommendations came after 15 months of consultation with leading AI experts in commercial industry, government, academia and the American public that resulted in a rigorous process of feedback and analysis among the nation’s leading AI experts with multiple venues for public input and comment. The adoption of AI ethical principles aligns with the DOD AI strategy objective directing the U.S. military lead in AI ethics and the lawful use of AI systems.
2020-03-02 08:53:20+00:00 Read the full story…
Weighted Interest Score: 4.6243, Raw Interest Score: 1.5424,
Positive Sentiment: 0.4499, Negative Sentiment 0.0000
Syncsort Partners with Databricks to Support Cloud Initiatives
Syncsort is partnering Databricks to support cloud initiatives for critical mainframe and IBM i data, enabling enterprises to leverage Syncsort Connect products to access, transform, and deliver mainframe data to Delta Lake.
Organizations rely on Databricks to process massive amounts of data in the cloud and power AI, machine learning and business insights. Syncsort Connect features a design once, deploy anywhere architecture that provides a graphical interface to deploy mainframe to cloud data transformation pipelines.
Integration with Syncsort Connect products enables the combination of the Databricks platform with Syncsort’s unrivaled ability to integrate previously inaccessible mainframe and IBM i data for analytics and data science.
2020-02-24 00:00:00 Read the full story…
Weighted Interest Score: 4.3342, Raw Interest Score: 2.1038,
Positive Sentiment: 0.1791, Negative Sentiment 0.2686
Qlik Expands Partnership With Databricks by Joining its Data Ingestion Network
According to a recent press release, “Qlik today announced it has expanded its partnership with Databricks, joining Databricks’ Data Ingestion Network. Qlik Data Integration simplifies loading data into Delta Lake, an open source project that provides reliable data lakes at scale, accelerating the creation of lakehouses for analytics and machine learning (ML). Lakehouse, a new data management paradigm, combines elements of data lakes and data warehouses, enabling business intelligence (BI) and ML on all of a business’s data.”
Itamar Ankorion, SVP of Technology Alliances at Qlik, commented, “We’re excited to be one of the inaugural partners for the launch of Databricks’ Data Ingestion Network. This is the latest development in our growing relationship focused on helping enterprises accelerate time to value with data in the cloud.. Our deeper integration provides Databricks’ customers with a more seamless on-ramp of data from any enterprise data source to their Delta Lake, with the best-fit modern data integration strategy to fuel future targets as their data platforms evolve.”
2020-02-28 08:05:33+00:00 Read the full story…
Weighted Interest Score: 4.1667, Raw Interest Score: 2.2596,
Positive Sentiment: 0.4056, Negative Sentiment 0.0000
Databricks, Partners, Open a Unified ‘Lakehouse’
Coalescing around an open source storage layer, Databricks is pitching a new data management framework billed as combining the best attributes of data lakes and warehouses into what the company dubs a “lakehouse.”
The new data domocile is promoted as a way of applying business intelligence and machine learning tools across all enterprise data. The company and its lakehouse partners also have assembled a “data ingestion network” that allows users to load siloed data into Delta Lake, a storage layer released by Databricks to the open source community last year.
Among the applications that can be integrated into the lakehouse are Google analytics, Salesforce and SAP along with Cassandra, Kafka, Oracle, MySQL and MongoDB databases. Those along with mainframe and file data would be available in one location for BI and machine learning use cases.
2020-02-24 00:00:00 Read the full story…
Weighted Interest Score: 3.5194, Raw Interest Score: 1.8564,
Positive Sentiment: 0.0913, Negative Sentiment 0.1826
Experts Debunk The Biggest Myths About AI In Business
Themarket for AI is growing at an unprecedented rate. Market Watch released a report last year showing that the market size is growing about 55% a year over the course of the decade between 2016 and 2025. The sudden growth of AI is not at all surprising. Businesses in all industries are starting to realize the potential AI (artificial intelligence) can bring to their sectors, strengthening decision-making while automating complex and time-consuming tasks.
Through the power of artificial intelligence, businesses can help to streamline their operations, increasing efficiency and overall productivity. Companies are now increasing the adoption of this technology in a range of different industries, which covers diverse sectors such as healthcare, finance, marketing and more. Through the incorporation of AI, industries have seen major shifts in how they run. While the true potential of AI is now being recognized by businesses from all different sectors, many myths have floated around causing scepticism and unnecessary fear over this transformative technology. If AI is to reach its true potential in businesses across all industries, it’s important to explore, and further debunk, these common misconceptions.
- AI Will Steal Our Jobs
- AI Is Hard to Integrate with Business
- Businesses Don’t Need AI
- AI is a Net Positive – Not a Destructive Force to the Economy
2020-02-28 17:05:53+00:00 Read the full story…
Weighted Interest Score: 4.2568, Raw Interest Score: 1.4572,
Positive Sentiment: 0.2732, Negative Sentiment 0.2049
CNAS Announces New Members of AI Task Force
The Center for a New American Security (CNAS) is pleased to announce the addition of three new members to the Task Force on Artificial Intelligence (AI) and National Security. The AI Task Force examines how the United States should respond to the national security challenges AI poses and is co-chaired by Robert O. Work, former Deputy Secretary of Defense, and Dr. Andrew W. Moore, Head of Google Cloud Artificial Intelligence.
The new AI Task Force members are…
2020-02-27 01:19:11+00:00 Read the full story…
Weighted Interest Score: 4.1733, Raw Interest Score: 1.9889,
Positive Sentiment: 0.0398, Negative Sentiment 0.3182
Top 3 Artificial Intelligence Research Papers – February 2020
At the beginning of every month, we decipher three research papers from the fields of machine learning, deep learning and artificial intelligence, that left the biggest impact on us in the previous month. Apart from that, at the end of the article, we add links to other papers that we have found interesting but were not in our focus that month. So, you can check those as well. In February, we explored papers that, as we see it, are going to leave a big impact on the future of machine learning and deep learning. In essence, we think that these proposals are going to change the way we do our jobs. Have fun!
- The Tree Ensemble Layer: Differentiability meets Conditional Computation
- The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
- SUOD: Toward Scalable Unsupervised Outlier Detection
2020-03-02 00:00:00 Read the full story…
Weighted Interest Score: 4.1407, Raw Interest Score: 1.8718,
Positive Sentiment: 0.1134, Negative Sentiment 0.2836
Common Pitfalls That The Deep Learning Startups Fail To Recognise
Today, AI’s significant applications are being recognised in the world when it comes to solving complex problems. AI and its branch, deep learning have considerably contributed across the sector with machine translation, natural language processing and computer vision. And over the last few years, we have witnessed a spur in deep learning startups, but like any other software-based ones, these startups encounter some pitfalls, but these mistakes are a little unique to them.
- Not Investing Enough in Data and Powerful Processors
- Not Accounting for the Cloud Charges
- Expensive Data Cleansing
- The Edge Cases
- Hiring the Right People
2020-03-02 10:30:00+00:00 Read the full story…
Weighted Interest Score: 4.0104, Raw Interest Score: 2.2657,
Positive Sentiment: 0.2201, Negative Sentiment 0.3755
Micron Bridges Memory Bandwidth Gap for ML
Deep learning accelerators based on chip architectures coupled with high-bandwidth memory are emerging to enable near real-time processing of machine learning algorithms. Memory chip specialist Micron Technology argues that hardware accelerators linked to higher memory densities are the best way to accelerate “memory bound” AI applications.
Micron (NASDAQ: MU) announced a partnership this week with German automotive supplier Continental (OTCMKTS: CTTAY) to adapt the memory maker’s deep learning accelerator to machine learning auto applications. The partners said they would focus on vehicle automation systems.
Micron’s deep learning accelerator also has machine learning applications for other edge deployments where memory bandwidth used for local data processing has so far failed to keep pace with microprocessor core growth.
2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 3.8473, Raw Interest Score: 2.0243,
Positive Sentiment: 0.2180, Negative Sentiment 0.0623
Talend Data Fabric’s Latest Version Features Variety of Self-Service Enhancements
Talend, a provider of cloud data integration and data integrity, is introducing the Winter ‘20 release of Talend Data Fabric, unveiling the new Talend Cloud Data Inventory.
Talend Cloud Data Inventory automatically calculates the Data Intelligence Score of all data across an organization and presents it in a service-self cloud app for every user.
Winter ’20 also provides several other capabilities including AI features and cutting-edge cloud co…
2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 3.7983, Raw Interest Score: 2.0854,
Positive Sentiment: 0.4373, Negative Sentiment 0.0336
SimCorp Launches New ML Initiative With Alkymi
Corp, a leading provider of investment management solutions and services to the global financial services industry, today announces a partnership with New York based start-up, Alkymi, to launch a new Machine Learning (ML) initiative. It’s arrival comes as institutional investors raise a number of data concerns, including the ability to quickly extract insights from unstructured data, for faster, more informed decision-making.
Many investment firms are currently buckling under a number of operational constraints, including the burden of processing unstructured data or outsourcing it, inadequate cross-asset co…
2020-02-26 14:30:54+00:00 Read the full story…
Weighted Interest Score: 3.7037, Raw Interest Score: 2.3941,
Positive Sentiment: 0.5064, Negative Sentiment 0.1842
How Going All-In on Machine Learning Changed Data Collection at Morningstar
Shariq Ahmad set an ambitious goal for Morningstar’s data collection team in 2019: to have at least 50 percent of its engineers working on machine learning initiatives by year’s end.
Ahmad joined Morningstar, which provides research and proprietary tools to investors, in 2010 and stepped into the role of head of technology for the data collection group in the summer of 2018. His first order of business was to automate the data collection process which, up until that point, had relied on analysts to gather information from numerous sources — ranging from SEC filings to managed investment documents — and verify its quality.
“Collecting financial data from various sources is an exhaustive process,” Ahmad said. “With an ever-increasing demand for new datasets, I realized we needed some form of automation to help us scale.”
2020-02-28 00:00:00 Read the full story…
Weighted Interest Score: 3.5430, Raw Interest Score: 1.9403,
Positive Sentiment: 0.1455, Negative Sentiment 0.2183
5 Key Differences Between a Data Lake vs Data Warehouse
A data lake is not a direct replacement for a data warehouse; they are supplemental technologies that serve different use cases with some overlap. Most organizations that have a data lake will also have a data warehouse. Let’s compare the properties of a data lake in comparison to a (data warehouse & separate ETL server).
- Data in data lakes is stored in its native format
- Data in data lakes can be accessed flexibly
- Data lakes provide schema-on-read access
- Data lakes provide decoupled storage and compute
- Data lakes go with cloud data warehouses
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.4128, Raw Interest Score: 1.7946,
Positive Sentiment: 0.1765, Negative Sentiment 0.0588
Actionable big data: How to bridge the gap between data scientists and engineers
The buzz around big data has created a widespread misconception: that its mere existence can provide a company with actionable insights and positive business outcomes.
The reality is a bit more complicated. To get value from big data, you need a capable team of data scientists to sift through it. For the most part, corporations understand this, as evidenced by the 15x – 20x growth in data scientist jobs from 2016 to 201…
2020-02-29 00:00:00 Read the full story…
Weighted Interest Score: 3.3882, Raw Interest Score: 1.9234,
Positive Sentiment: 0.4075, Negative Sentiment 0.2445
How Chicago Tech Companies Use AI to Drive Decision-Making
Humans are inferior to technology when it comes to making objective decisions. According to Harvard Business Review, cognitive biases heavily influence judgment, often steering us away from objective decision-making. Companies are now turning to artificial intelligence to optimize their data-based business decisions. AI-driven workflows crunch data, consolidate insights and provide best possible outcomes, saving humans time, money and of course, error.
At dealmaking software company Ansarada, Vice President of Sales, Americas Sean Elder said their AI Bidder Engagement Score assesses 57 separate data metrics to determine a bidder’s behavior. “This allows dealmakers — whose time is stretched thin during these events — to prioritize the serious bidders and focus their time and energy where it counts,” said Elder.
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.3720, Raw Interest Score: 1.8073,
Positive Sentiment: 0.3772, Negative Sentiment 0.1257
Intel uses AI to find new customers in specific industries
How does Intel, which expects the market opportunity for AI hardware to grow from $2.5 billion in 2017 to $10 billion in 2022, find new customer opportunities? With AI, of course. In a blog post today, Intel detailed a tool its IT Advanced Analytics team developed internally to mine millions of public business pages and extract actionable segmentation for both current and potential customers. The chipmaker says that its sales and marketing staff have used the new system to discover new leads faster and more accurately than before.
“Intel sales and marketing staff have traditionally used manual search and vendor tools in order to identify potential leads; however, these methods lack the ability to align with the internal language used by Intel staff to properly segment and tailor their outreach plans,” wrote Intel. “Additionally, in the era of globalized business, existing customers are often expanding into new domains, requiring sales and marketing staff to constantly keep current with changes in a wide variety of industries.”
As Intel explains it, the system focuses on two key classification aspects: (1) an industry segment ranging from verticals such as “healthcare” to more specific fields such as “video analytics” and (2) functional roles like “manufacturer” or “retailer” that further distinguish potential sales and marketing opportunities. The AI model acquires a constant stream of textual data from millions of sites, updating the multi-million node knowledge graph with gigabytes of data every hour, which then gets passed along to a set of machine learning models for segmenting potential customers.
2020-02-27 00:00:00 Read the full story…
Weighted Interest Score: 3.2248, Raw Interest Score: 1.5195,
Positive Sentiment: 0.3482, Negative Sentiment 0.0317
Machine Learning on the Edge, Hold the Code
Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that’s attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code.
Qeexo emerged from Carnegie Mellon University in 2012, just at the dawn of the big data age. According to Sang Won Lee, the company’s co-founder and CEO, the original plan called for Qeexo to be a machine learning application company.
The company landed a big fish, the Chinese mobile phone manufacturer Huawei, right out the gate. Huawei liked the ML-based finger-gesture application that Qeexo (pronounced “Key-tzo”) developed, and the company wanted Qeexo to ensure that it could run across all of its phone lines. That was a good news-bad news situation, Lee says.
“Our first commercial implementation with Huawei kept the whole company in China for two months, to finish one model with one hardware variant,” Lee tells Datanami. “We came back and it was difficult to keep the morale high for our ML engineers because nobody wanted to constantly go abroad to do this type of repetitive implementation.
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 3.0297, Raw Interest Score: 1.7888,
Positive Sentiment: 0.1750, Negative Sentiment 0.0972
AI Weekly: U.S. and EU strike contrasting tones on AI regulatory policy
This week, the White House Office of Science and Technology Policy (OSTP) released a year-one report card on its American Artificial Intelligence Initiative. Earlier this month, the European Commission (EC) published a major set of proposals for its strategy on AI. Both of these follow AI principles and regulations proposed in May 2019 by the multi-nation Organization for Economic Co-operation and Development (OECD), which includes the U.S. and European countries.
Despite that shared international work, the U.S. and Europe have also gone their own respective ways. It’s clear that the rhetoric of both is strongly bound to geography — U.S.-first here, Europe-first there — but the aforementioned announcements also show a slight but important difference in tone between the two. Whereas Europe sounds largely optimistic, the U.S. comes off as more fearful.
Just over a week ago, EC president Ursula von der Leyen took to the podium and gave a speech announcing and explaining Europe’s new AI strategy. She discussed Eurocentric concerns first, adding, “We want the digital transformation to power our economy, and we want to find European solutions in the digital age.”
2020-02-28 00:00:00 Read the full story…
Weighted Interest Score: 2.9688, Raw Interest Score: 1.1739,
Positive Sentiment: 0.2594, Negative Sentiment 0.3140
Freshworks To Improve Customer Experience Through Acquisition Of AI Startup AnsweriQ
Freshworks — a SaaS-based company — has acquired Seattle-based AI startup AnsweriQ for an undisclosed amount. Founded in 2015, Freshworks is now valued at $3.5 billion and has closed about 10 acquisitions since then. The company offers a wide range of products such as freshdesk, freshsales, freshchat, among others. And its platforms include Freddy AI — an analytics solution — that provides predictive insights across the customer journey.
2020-02-28 02:46:55+00:00 Read the full story…
Weighted Interest Score: 2.9187, Raw Interest Score: 1.2440,
Positive Sentiment: 0.4306, Negative Sentiment 0.0957
Real-time Data Streaming, Kafka,and Analytics Part 2: Going Beyond Pure Streaming
Data transaction streaming is managed through many platforms, with one of the most common being Apache Kafka. In our first article in this data streaming series, we delved into the definition of data transaction and streaming and why it is critical to manage information in real-time for the most accurate analytics. As more individuals increase their data literacy and use data to make business decisions, real-time data is becoming a critical factor. To handle this effectively, companies are implementing modern data architectures that can support this real-time requirement, including change data capture (CDC) and Apache Kafka as their streaming platform components of choice.
Apache Kafka is a strong choice to handle real-time data streaming as it ingests, persists and presents streams of data for consumption and use by individuals for analytics. Basically, Kafka operates through three basic components to move data in real-time: producers, brokers and consumers. The producer is a process that writes or sends the data to Kafka. It is then sent along to the Kafka broker, which runs the process and responds to requests from products and consumers. Finally, the consumer is the end process – an application program that reads the records at the end of the stream.
2020-02-25 00:00:00 Read the full story…
Weighted Interest Score: 2.8375, Raw Interest Score: 1.8397,
Positive Sentiment: 0.2339, Negative Sentiment 0.0780
Unveiling The IT Stack To Support The Artificial Intelligence Of Things At CES 2020
Flying taxis, concept cars, curved screens, and folding PCs are lighting up Las Vegas nights. This is a place where the world’s best tech gets unveiled. As intelligence will transform most verticals, including transportation, smart home, healthcare, and public services, emerging technologies such as 5G, the internet of things (IoT), and edge computing are forming the foundation of and heating up the data economy to support pervasive AI-enabled applications.
Intelligence Goes Vertical And Pervasive. Vendors at CES are actively embedding AI into their products. Open source frameworks and AI suites available on public clouds have lowered the barrier to develop AI applications and empower products with intelligence. To win against fierce competition, however, firms must differentiate their intelligence with exceptional performance, identify the problems created by increasing customer expectations, and solve them with smart algorithms. At this stage, even small steps garner huge investment.
2020-02-28 02:09:36-05:00 Read the full story…
Weighted Interest Score: 2.8306, Raw Interest Score: 1.3927,
Positive Sentiment: 0.3296, Negative Sentiment 0.2143
Airlines take no chances with our safety. And neither should artificial intelligence
You would thinking flying in a plane would be more dangerous than driving a car. In reality it’s much safer, partly because the aviation industry is heavily regulated. Airlines must stick to strict standards for safety, testing, training, policies and procedures, auditing and oversight. And when things do go wrong, we investigate and attempt to rectify the issue to improve safety in the future.
It’s not just airlines, either. Other industries where things can go very badly wrong — such as pharmaceuticals and medical devices — are also heavily regulated. Artificial intelligence is a relatively new industry, but it’s growing fast and has great capacity to do harm. Like aviation and pharmaceuticals, it needs to be regulated.
2020-03-02 11:30:00+11:00 Read the full story…
Weighted Interest Score: 2.7745, Raw Interest Score: 1.0875,
Positive Sentiment: 0.1338, Negative Sentiment 0.4517
Data Versioning Matters to Data Science
Amazon Web Services (AWS) recently published a case study about how the Allen Institute for Cell Science — which was founded by Microsoft’s Paul Allen to research how the human brain works in health and disease — is taking new steps to make its data and metadata easy to access, search, and redistribute for internal and external users on the web.
The Institute has a lot of data: more than 7 terabytes and over 288,000 objects on the Amazon S3 web …
2020-02-27 08:35:45+00:00 Read the full story…
Weighted Interest Score: 2.7371, Raw Interest Score: 1.5520,
Positive Sentiment: 0.1207, Negative Sentiment 0.1035
How robots explain themselves matters more than you might think
Artificial intelligence is entering our lives in many ways—on our smartphones, in our homes, in our cars. These systems can help people make appointments, drive, and even diagnose illnesses. But as AI systems continue to serve important and collaborative roles in people’s lives, a natural question is: Can I trust them? How do I know they will do what I expect?
Explainable AI (XAI) is a branch of AI research that examines how artificial agents can be made more transparent and trustworthy to their human users. Trustworthiness is essential if robots and people are to work together. XAI seeks to develop AI systems that human beings find trustworthy—while also performing well to fulfill designed tasks.
At the Center for Vision, Cognition, Learning, and Autonomy at UCLA, we and our colleagues are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. Our lab uses a type of knowledge representation—a model of the world that an AI uses to interpret its surroundings and make decisions—that can be more easily understood by humans. This naturally aids in explanation and transparency, thereby improving trust of human users.
2020-02-29 07:00:44 Read the full story…
Weighted Interest Score: 2.7027, Raw Interest Score: 1.3343,
Positive Sentiment: 0.3160, Negative Sentiment 0.1404
All Machine Learning Products Launched By Google In February 2020
When it comes to artificial intelligence, it is hard to keep Google away from bringing in a new array of services and products on a regular basis. In the month of January, the tech giant launched a number of products such as LaserTagger, Meena and Reformer, to name a few. Just like the previous month, Google has rolled down a number of new tools/techniques to look at, which will benefit a host of people in regard to artificial intelligence and other related streams.
Here is a list of the products launched by Google in February 2020:
- T5: The Text-To-Text Transfer Transformer
- AutoFlip: An Open-Source Framework For Intelligent Video Reframing
- Learning To See Transparent Objects Via ClearGrasp
- Setting Fairness Goals With The TensorFlow Constrained Optimisation Library
- Generating Diverse Synthetic Medical Image Data For Training Machine Learning Models
2020-03-01 04:30:00+00:00 Read the full story…
Weighted Interest Score: 2.6724, Raw Interest Score: 1.5215,
Positive Sentiment: 0.2355, Negative Sentiment 0.2174
Busted! New Ways Your Boss Knows You’re About to Quit
Are you conveying a more subdued emotional tone in your emails to your boss? Have you increased the distance between yourself and your colleagues when chatting around the coffee machine? Today, changing your routines and habits (even in small ways) may peg you as a flight risk.
Don’t worry: If your boss suspects you’re about to quit, they haven’t become a soothsayer. Chances are good they’re being tipped off by HR and a burgeoning practice that uses data analytics and machine-learning algorithms to predict in real time which employees are likely to leave. What’s more, the highly accurate “quit algorithms” can reveal your intentions even before you start accepting calls from recruiters or post your résumé.
Here are the new ways that your boss can identify you as someone who is likely to quit…
2020-02-26 00:00:00 Read the full story…
Weighted Interest Score: 2.6404, Raw Interest Score: 1.3101,
Positive Sentiment: 0.1872, Negative Sentiment 0.2433
The Challenges Of Building Inferencing Chips
Putting a trained algorithm to work in the field is creating a frenzy of activity across the chip world, spurring designs that range from purpose-built specialty processors and accelerators to more generalized extensions of existing and silicon-proven technologies.
What’s clear so far is that no single chip architecture has been deemed the go-to solution for inferencing. Machine learning is still in its infancy, and so is the entire edge concept where most of these inferencing chips ultimately will be deployed. Moreover, how to utilize this technology across multiple end markets and use cases, let alone choose the best chip architectures, has shifted significantly over the past 12 to 18 months as training algorithms continue to evolve. That makes it difficult, if not impossible, for any single architecture to dominate this field for very long.
“Machine learning can run on a range of processors, depending on what you are most concerned about,” said Dennis Laudick, vice president of marketing for the machine learning group at Arm. “For example, all machine learning will run on an existing CPU today. Where you only want to do light ML, such as keyword spotting, or where response time is not critical, such as analyzing offline photos, then the CPU is capable of doing this. It can still carry out other tasks, which cuts the need for additional silicon investment. Where workloads become heavier, and where performance is critical or power efficiency is a concern, then there are a range of options.”
2020-02-27 08:02:48+00:00 Read the full story…
Weighted Interest Score: 2.6375, Raw Interest Score: 1.3795,
Positive Sentiment: 0.2024, Negative Sentiment 0.1125
In AI, the objective is subjective!
What is “Ground Truth” in AI? (A warning.) : A demo that shows why you shouldn’t treat AI like a magical box of magic
With all the gratuitous anthropomorphization infecting the machine learning (ML) and artificial intelligence (AI) space, many businessfolk are tricked into thinking of AI as an objective, impartial colleague that knows all the right answers. Here’s a quick demo that shows you why that’s a terrible misconception.
A task that practically every AI student has to suffer through is building a system that classifies images as “cat” (photo contains a cat) or “not-cat” (no cat to be seen). The reason this is a classic AI task is that recognizing objects is a task that’s relatively easy for humans to perform, but it’s really hard for us to say how we do it (so it’s difficult to code explicit rules that describe “catness”). These kinds of tasks are perfect for AI.
2020-02-28 21:32:23.975000+00:00 Read the full story…
Weighted Interest Score: 2.6358, Raw Interest Score: 1.0379,
Positive Sentiment: 0.1800, Negative Sentiment 0.2118
Scrabble Chinese Room and AI Understanding
By Lance Eliot, the AI Trends Insider
If you are a Scrabble fan, you might remember the headlines in 2015 that blared that the winner of the French Scrabble World Championship was someone that did not understand a word of French.
Note that I spelled this stereotypical French phrase as it is spelled in the French language, as one word, rather than the Americanized version of two words with the accent (sacre bleu), which would be impo…
2020-02-25 12:40:07+00:00 Read the full story…
Weighted Interest Score: 2.5648, Raw Interest Score: 0.9780,
Positive Sentiment: 0.2404, Negative Sentiment 0.1064
Deep Transfer Learning for Image Classification
tep-by-step tutorial from data import to accuracy evaluation
The following tutorial covers how to set up a state of the art deep learning model for image classification. The approach is based on the machine learning frameworks “Tensorflow” and “Keras”, and includes all the code needed to replicate the results in this tutorial (unfortunately, the syntax when including code blocks in medium articles does not look very nice, but it should hopefully be readable).
The prerequisites for setting up the model is access to labelled data, and as an example case I have used images of various traffic signs (which can b…
2020-02-28 15:12:12.983000+00:00 Read the full story…
Weighted Interest Score: 2.4999, Raw Interest Score: 1.3773,
Positive Sentiment: 0.0915, Negative Sentiment 0.1706
Addressing Catastrophic Forgetting In ML With ANWL & Meta-Learning
eural networks sometimes suffer from forgetting the last tasks it has done upon learning new information, something which is called catastrophic forgetting.
This catastrophic forgetting prevents the machine learning systems from ‘continual learning which is the ability to remember previous tasks while still learning new things. But all hope is not lost, some systems can still be trained to remember, enter, ANML (a neuromodulated meta-learning algorithm).
What is ANML?
While a lot of work has been done on keeping the machine learning models from catastrophically forgetting the previous knowledge, all of it …
2020-02-27 13:30:00+00:00 Read the full story…
Weighted Interest Score: 2.4840, Raw Interest Score: 1.4458,
Positive Sentiment: 0.2370, Negative Sentiment 0.3792
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