AI & Machine Learning News. 23, 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?
Harvard’s (free) Introduction to Artificial Intelligence with Python, with CS50’s own Brian Yu.
Hello, World : This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
CloudQuant Thoughts : With little news this week that was not Corona Virus related I had to look long and hard to find something interesting for you. And at the last moment I discovered that the CS50 team at Harvard had just launched an excellent FREE introduction to AI in Python. Enjoy!
Growth in Machine Learning Leading to Demand for Automated ML
Machine learning has been used successfully in many disciplines that increasingly depend on it. However, the success relies on human machine learning experts to perform many tasks, according to an account on AutoML.org, a website of the community. These tasks include: Preprocessing and cleaning the data; selecting and constructing appropriate features; selecting an appropriate model family; optimizing model hyper parameters; post-processing machine learning models; and critically analyzing the results.
The growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used more easily and without necessarily expert knowledge. The goal is to progressively automate these manual tasks in what is being called AutoML. Most companies offering AutoML solutions are positioning them as tools to increase the production of data scientists, and to simplify the process to make it more accessible to new AI developers, according to an account in Towards Data Science written by Justin Tennenbaum, a data scientist.
2020-03-19 21:30:38+00:00 Read the full story…
Weighted Interest Score: 3.4601, Raw Interest Score: 1.8878,
Positive Sentiment: 0.3464, Negative Sentiment 0.1039
CloudQuant Thoughts : Perhaps the most important note in this article is the concern from Microsoft that AutoML may increase the risk of Over Fitting. “In the most egregious cases, an over-fitted model will assume that the feature value combinations seen during training will always result in the exact same output for the target.”
Turing Award For Pixar, EfficientNet Lite Release And More:Top AI News
Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. So, here’s a compilation of all the important releases for the ML developers from top companies like Google and Uber.
Here’s what is new this week:
- Google Open-Sources Neural Tangent Library
- 3D Object Detection With MediaPipe
- EfficientNet-Lite For Mobiles By TensorFlow
- Introducing Piranha: An Open-Source Tool to Automatically Delete Stale Code
- Pixar’s Pioneers Get 2019 Turing Award
- AlphaGo The Movie
2020-03-20 03:30:00+00:00 Read the full story…
Weighted Interest Score: 2.3338, Raw Interest Score: 1.5695,
Positive Sentiment: 0.3176, Negative Sentiment 0.1495
CloudQuant Thoughts : This is a nice summary of the week in AI with some interesting articles.
Multi-Agent Seasonal Dataset For Autonomous Car Development by Ford
The automotive industry has been working hard for a few years now on one of the most challenging problems of transportation, which is a fully autonomous self-driving vehicle. Currently, the autonomous systems use a combination of 3D scanners, high-resolution cameras and GPS/INS, to enable autonomy. However, in order to deal robustly on roads, handle a number of scenarios and maintain operating conditions, these systems will have to evolute into multi-agent autonomous systems.
Tech giants such as Apple, Facebook, Microsoft, among others, have been developing intelligent machine learning models to reduce collisions of self-driving cars. However, in all these years, Ford has always been on the quieter side when it comes to being open regarding their plans on autonomous driving projects until now.
Recently, researchers from the multinational automaker, Ford launched a challenging multi-agent seasonal dataset for autonomous cars. This multi-agent autonomous vehicle data presents the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environment.
2020-03-23 12:06:15+00:00 Read the full story…
Weighted Interest Score: 4.1347, Raw Interest Score: 1.3129,
Positive Sentiment: 0.1239, Negative Sentiment 0.1486
Top Hyperparameter Optimisation Tools For Your Machine Learning Models
A key balancing act in machine learning is choosing an appropriate level of model complexity: if the model is too complex, it will fit the data used to construct the model very well but generalise poorly to unseen data (overfitting). And if the complexity is too low, the model won’t capture all the information in the data (underfitting).
In a deep learning context, a model’s performance depends heavily on the hyperparameter optimisation, given that the vast search space of features, evaluation of each configuration can be expensive.
Generally, there are two types of toolkits for HPO: open-source tools and services that rely on cloud computing resources.
In the next section, we list down a few tools that have helped in making hyperparameter optimisation easier:
2020-03-23 09:30:20+00:00 Read the full story…
Weighted Interest Score: 4.0618, Raw Interest Score: 1.5456,
Positive Sentiment: 0.1482, Negative Sentiment 0.0847
How AI Can Revolutionize Banking
AI brings the potential for disruption and transformation due to its ability to make decisions and take action much quicker than its human counterparts. It has been seen as a means of increasing productivity within a company and improving revenues through better customer engagements.
But the use of AI is not without pitfalls, risks and detractors. Will AI discriminate between classes of people? Will AI used for good or just corporate greed? How should the use of AI be regulated?
To discuss the opportunities and challenges of AI in banking, we interviewed Dan Faggella, founder and CEO of the artificial intelligence research agency, Emerj. Dan is a globally recognized speaker on the use-cases of artificial intelligence in business, and has presented to the World Bank, the United Nations, INTERPOL, and global banking companies.
2020-03-17 06:00:44+00:00 Read the full story…
Weighted Interest Score: 3.8479, Raw Interest Score: 1.9483,
Positive Sentiment: 0.3172, Negative Sentiment 0.1812
AI at the Edge Enabling a New Generation of Apps, Smart Devices
Enabling an edge-computing architecture with AI is seen as a way forward for advances in strategic applications. And at the advent of 5G network speeds, AI is seen as essential to the endpoints.
A new network paradigm based on virtualization enabled by Software Defined Networking (SDN) and Network Function Virtualization (NFV), presents an opportunity to push AI processing out to the edge in a distributed architecture, suggests a recent report from Strategy Analytics.
Three types of edge computing are foreseen: device as the edge, in which an IoT device generates and consumes data and has embedded AI that can send and receive data to and from additional AI systems; enterprise premise network edge, that can support AI processing on a piece of hardware in a vehicle, drone or machinery, and can collect and process data from smart devices; and operator network edge, with an AI stack/platform to host applications and services, which may be located at a micro data center in a radio tower, edge router, base station or internet gateway.
2020-03-19 21:30:02+00:00 Read the full story…
Weighted Interest Score: 3.8267, Raw Interest Score: 2.0214,
Positive Sentiment: 0.2297, Negative Sentiment 0.2144
Is Python storming ahead of Java in fintech?
The use of Python is catching up to Java in banking and fintech applications, but what are the reasons behind the emergence of Python? While three million developers have joined the Java community in the past year, in the banking sector, Python is fast closing in on Java’s position in top spot.
Python’s backstory in banking. Across all sectors, Python has reached seven million active developers fuelled in part by a staggering 62% of machine learning developers and data scientists who now use the programming language. This popularity gathered momentum back in 2015, with numerous financial institutions hiring Python developers. At around the same time, the sheer volume of fintechs – both funded growth businesses and bootstrapping startups – also started to make their presence felt in the developer skills marketplace. Despite its recent popularity, particularly across the investment banking and hedge fund industries, Python is not a new language. The first versions of Python emerged in 1991, five years before HTTP 1.0 and four years before Java.
2020-03-18 00:00:00 Read the full story…
Weighted Interest Score: 3.7964, Raw Interest Score: 2.0435,
Positive Sentiment: 0.3081, Negative Sentiment 0.1746
Dataiku 7 Brings Deeper Collaboration and More Granular Explainability to Enterprise AI
Today Dataiku, the leading Enterprise AI and machine learning platform, announced the release of Dataiku 7, bringing deeper integration for technical data professionals to work on machine learning project development and row-level explainability for white-box AI. Additional feature highlights with this latest release include Kubernetes-powered web apps to expand on the capabilities introduced in Dataiku 6 and a machine learning-assisted data labeling plugin.
“Collaboration has been at the core of Dataiku since our founding in 2013, and with Dataiku 7, we’re continuing to add features that deepen our philosophy to effectively democratize AI in the enterprise”
2020-03-20 07:15:09+00:00 Read the full story…
Weighted Interest Score: 3.6422, Raw Interest Score: 1.6196,
Positive Sentiment: 0.2776, Negative Sentiment 0.1388
Hitchhiker’s guide to learning Data Science
With companies raising huge funds using the term “Data Science”, the grounds for the value of the skill has been established for quite some time now. Billions are being invested to hire talented Data Scientists who can build a state of the art deduction machines putting zettabytes of data to use. The revolution can now be witnessed as our lives are…
2020-03-23 12:28:09.894000+00:00 Read the full story…
Weighted Interest Score: 3.3169, Raw Interest Score: 1.7397,
Positive Sentiment: 0.2060, Negative Sentiment 0.2060
What is the Future of Machine Learning?
Machine Learning has been one of the hottest topics of discussion among the C-suite. The blog speaks about the future of Machine Learning. Read this to know more.
With its incredible potential to compute and analyze huge amounts of data, advanced ML techniques are being used in businesses to perform complex tasks quicker and more efficiently.
The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 20…
2020-03-21 16:05:43.755000+00:00 Read the full story…
Weighted Interest Score: 3.2152, Raw Interest Score: 1.8626,
Positive Sentiment: 0.2714, Negative Sentiment 0.0123
Building a Winning Multi-Asset Execution Strategy
The road to digitization is paved with promises. Great stuff–like cost efficiencies, speed, smart automation, decision-ready intelligence, and real-time optimization. With payoffs so attractive, our industry has been pursuing greater digitization for quite some time. Despite our obsession with speed however, we’ve largely overlooked the more straightforward route-learning from the native digital companies who paved the way in the first place. If the buy-side wants to make the most of digital’s next level possibilities, we should take a page from these companies’ innovation roadmap. Instead of reacting to the acceleration of technology and the proliferation of venues, we can proactively transform to not only keep up with but capitalize on, the pace of change.
2020-03-23 01:42:04+00:00 Read the full story…
Weighted Interest Score: 3.0863, Raw Interest Score: 1.7499,
Positive Sentiment: 0.4541, Negative Sentiment 0.0222
Are AI and Machine Learning the Key to Understanding the U.S. Economy?
GPUs fuel AI and machine learning. Initially created for video games, they are used in sports and business analysis by fantasy baseball enthusiasts, oddsmakers, and front office executives who want to enhance their understanding of the hidden value of often obscure players. Other uses of this technology’s extreme processing power include the recognition of animals, such as dog breeds or endangered species, to allow biologists to gain a more accurate understanding of species populations in a geographical area.
GPUs and advanced statistics constitute an incredible advancement in 21st-century technology and can result in a more precise and deeper understanding of the data that is captured by a plethora of sources. So, why is so much of the economic data used by the news media and the U.S. Bureau of Labor Statistics for unemployment or gross domestic product (GDP) analysis still the same as what was used decades ago?
The initial concept of the GDP (originally referred to as gross national product [GNP] and calculated slightly differently than GDP) was first conceptualized in the 17th century. The GDP measures the value of all goods and services produced within a country’s border within a specific point in time. The modern version of the GDP was developed in 1934 by Simon Kuznets for a U.S. Congress report to measure the U.S. recovery from the Great Depression. Kuznets’ work ultimately resulted in a Nobel Prize in Economics.
2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 3.0332, Raw Interest Score: 1.4432,
Positive Sentiment: 0.2698, Negative Sentiment 0.3777
OANDA Launches MT5 Support for Clients in Japan
OANDA, a leading player in online multi-asset class trading and data analytics sector revealed the launch of new platform support for its products. As per statement released by the firm, the New York headquartered firm will now offer MT5 Support trading platform to its clients who are based in Japan. The service was initially made available for use of its clients for demo accounts back in December of 2019, but it has now made the service accessible for its clients who hold …
2020-03-22 16:29:56+00:00 Read the full story…
Weighted Interest Score: 2.9299, Raw Interest Score: 1.4316,
Positive Sentiment: 0.2444, Negative Sentiment 0.0000
The Chief Data Officer and the Chief Digital Officer: Work Together, Not Apart
Data vs. digital: That’s a big tension within many organizations.
Chief Data Officer s and Chief Digital Officers don’t always agree about some important things, said Joe Caserta, president of consulting firm Caserta, during his DATAVERSITY® Enterprise Data WorldConference presentation titled Building a Foundation for Disruption and Advanced Analytics. What’s the disconnect between the two roles that share the CDO acronym?
The Chief Digital Officer is really about the customer experience, about being the customer advocate. The Chief Digital Officer wants the customers to buy something as quickly and as easily as possible, no matter what device, and to capture and share the data about the transaction with any other part of the application and with any other part of the business that’s going to make that experience better, Caserta said. And to do it all as quickly as possible.
2020-03-19 07:35:13+00:00 Read the full story…
Weighted Interest Score: 2.7915, Raw Interest Score: 1.5427,
Positive Sentiment: 0.2160, Negative Sentiment 0.1388
How Machine Learning Fights Financial Fraud
We have e-shops, online banking, online insurances, and tons of other online services. But there’s one more thing we have – online fraud, as powerful as ever.
Fraudsters take advantage of any weak spot they find to steal millions before security teams can see and patch up the breach. So companies are forced to look for new solutions to prevent, detect, and eliminate fraud. And machine learning seems to be the best answer to financial fraud. How does it work, what are the benefits, and who uses it?
2020-03-19 11:00:00+00:00 Read the full story…
Weighted Interest Score: 2.7804, Raw Interest Score: 1.4731,
Positive Sentiment: 0.2487, Negative Sentiment 0.6505
24 Best (and Free) Books To Understand Machine Learning
“What we want is a machine that can learn from experience” – Alan Turing
We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. There is no doubt that Machine Learning has become one of the most popular topics nowadays. According to a study, Machine Learning Engineer was voted one of the best jobs in the U.S. in 2019. Looking at this trend, we have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. Enjoy!
2020-03-24 00:00:00 Read the full story…
Weighted Interest Score: 2.7794, Raw Interest Score: 2.2373,
Positive Sentiment: 0.4195, Negative Sentiment 0.1199
Yes, You Can Do AI Without Sacrificing Privacy
In general, the more data you have, the better your machine learning model is going to be. But stockpiling vast amounts of data also carries a certain privacy, security, and regulatory risks. With new privacy-preserving techniques, however, data scientists can move forward with their AI projects without putting privacy at risk.
To get the low down on privacy-preserving machine learning (PPML), we talked to Intel’s Casimir Wierzynski, a senior di…
2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 2.7272, Raw Interest Score: 1.5198,
Positive Sentiment: 0.2266, Negative Sentiment 0.1600
Domo Shines a Light on Dark Data with New Augmented Capabilities in the Business Cloud
According to a recent press release, “Today Domo announced it is making it even easier to get BI leverage at cloud scale in record time through new augmented capabilities in the Domo Business Cloud. In a new Dimensional Research study sponsored by Domo, 92% of individuals surveyed said they’ve made decisions in the past three months without having all the information they wanted, with most reporting that data is just too hard to access. And while…
2020-03-20 07:10:25+00:00 Read the full story…
Weighted Interest Score: 2.6652, Raw Interest Score: 1.6676,
Positive Sentiment: 0.3891, Negative Sentiment 0.0556
Balancing Hard Data, Panic to Combat Pandemic
The early success of South Korea and Taiwan in slowing the spread of the novel coronavirus has underscored the necessity for a top-down, data-driven approach in which tech-savvy officials, prepared after earlier Asian epidemics, applied their know-how to mitigate a public health crisis.
While SARS-CoV-2 data trackers reported a sudden, sharp and unexplained spike in Taiwan infections on Thursday (March 19), South Korea’s infection rate continues…
2020-03-19 00:00:00 Read the full story…
Weighted Interest Score: 2.5754, Raw Interest Score: 1.4761,
Positive Sentiment: 0.1256, Negative Sentiment 0.4083
SoftBank to buy back $41 billion in assets to trim debt
BANKGOK (AP) — The Japanese technology and telecoms company SoftBank said Monday it plans to buy back up to 4.5 trillion yen ($41 billion) of its assets as it seeks to trim its gigantic debt burden.
The company’s founder, Masayoshi Son, said the move reflected “the firm and unwavering confidence we have in our business.”
Tokyo-based SoftBank will buy up to 2 trillion yen ($18.1 billion) of its shares, Son said in a statement. Earlier, SoftBank …
2020-03-23 00:00:00 Read the full story…
Weighted Interest Score: 2.5632, Raw Interest Score: 1.6968,
Positive Sentiment: 0.0722, Negative Sentiment 0.3249
IQ-AI’s Imaging Biometrics at the forefront of brain tumour analysis
What IQ-AI does:
( ) is the company formerly known as Flying Brands. The name change reflected its aspiration of becoming a leader in the field of medical imaging diagnostics. It currently comprises two businesses – Stone Checker Software and Imaging Biometrics (IB).
This predictive, diagnostic software product helps urologists determine whether a kidney stone will disintegrate under a vibration process called lithotripsy. This ca…
2020-03-18 00:00:00 Read the full story…
Weighted Interest Score: 2.5269, Raw Interest Score: 1.1293,
Positive Sentiment: 0.1882, Negative Sentiment 0.1076
Digital Outcomes Now Signs Strategic Partnership with Yellowbrick Data
A new press release reports, “The next wave of Mobile 5G and IOT capabilities will generate volumes of data unlike anything previously imaginable. The organizations who are able to process this data as fast as possible to create valuable outcomes will rule the digital future. ‘Yellowbrick Data has created a breakthrough in processing analytic datasets that is extremely well positioned to deliver on this coming 5G opportunity. In 1/20th the rack s…
2020-03-20 07:05:32+00:00 Read the full story…
Weighted Interest Score: 2.4987, Raw Interest Score: 1.4278,
Positive Sentiment: 0.6629, Negative Sentiment 0.0510
Startup Cuberg Uses AI To Build Energy Dense, Lightweight Batteries
Startup Cuberg is working on developing lighter, safer, more energy-dense batteries, and they’re using a machine learning platform developed by Aionics Technologies to do it faster. “The exciting thing we do is make batteries that are very energy dense. They are much lighter than lithium ion batteries but they have much more energy in them,” said Olivia Risset, PhD, senior scientist at Cuberg. The batteries that Cuberg makes are safer than lithium ion batteries because the liquid component, the electrolyte, is nonflammable as opposed to what has been used traditionally in lithium ion batteries. “Because of that,” says Risset, “electric aviation is a great place for us because they are very sensitive to weight, but also to safety.” Most of Cuberg’s current sales are within the drone industry, but their biggest investor is Boeing. “People talk about electric vehicles a lot, but electric aircraft and flying taxis are emerging tech that is coming soon. By 2022 there are going to be fleets of electric aircraft,” predicts Risset.
2020-03-19 21:30:16+00:00 Read the full story…
Weighted Interest Score: 2.4978, Raw Interest Score: 1.1231,
Positive Sentiment: 0.2695, Negative Sentiment 0.0898
Databricks Delivers Security and Scalability Enhancements
s, today announced new features within its platform that provide deeper security controls, proactive administration and automation across the data and ML lifecycle. As data teams enable analytics and machine learning (ML) applications across their organizations, they require the ability to securely leverage data at massive scale. Doing this can be complex and risky, especially when operating in a multi-cloud environment. Security is fragmented, which makes corporate access policies difficult to extend, administration is reactive and inefficient, and devops processes like user management or cluster provisionin…
2020-03-23 07:15:45+00:00 Read the full story…
Weighted Interest Score: 2.4490, Raw Interest Score: 1.7534,
Positive Sentiment: 0.3507, Negative Sentiment 0.3507
Unlock Machine Learning for the New Speed and Scale of Business
Vertica is transforming the way organizations build, train and operationalize machine learning models. Are you ready to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises?
Read our white paper to find out how you can bring predictive analytics projects to market faster than ever before with:
2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 2.3460, Raw Interest Score: 1.7699,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000
Behavox CEO explains backing from SoftBank’s Vision Fund 2
Behavox, a New York startup that uses artificial intelligence to scan employee conversations, said in a statement in February that it had raised $100 million from SoftBank’s Vision Fund 2.
Erkin Adylov, the company’s CEO and founder, told Business Insider he was originally hoping to raise between $25 million and $50 million in fall 2019.
to raise between $25 million and $50 million in fall 2019. Adylov said the Vision Fund 2 investors asked him: “What if …
2020-03-21 00:00:00 Read the full story…
Weighted Interest Score: 2.2601, Raw Interest Score: 1.3090,
Positive Sentiment: 0.2380, Negative Sentiment 0.1983
AI Weekly: How data scientists are helping to flatten the pandemic curve
In any time of trouble, the archetypal hero usually takes a specific form — soldiers in World War II, firefighters on 9/11, and now health care professionals in the time of COVID-19. But data scientists are playing an indispensable role in fighting the global pandemic. While medical professionals are on the front lines caring for the sick, data scientists have shouldered the responsibility of helping keep everyone else healthy by disseminating crucial information to the world.
A case in point is the “flatten the curve” mantra. Originating from the Centers for Disease Control (CDC), the idea is that we have to slow down rates of infection in order to keep health care systems from collapsing. Those three words are potentially going to save millions of lives. But they mean nothing without the simple graphs illustrating how slowed infections can improve outcomes, and the graphs don’t exist without data.
These visualizations tell a story of exponential growth that can be difficult for the average person to immediately comprehend. “COVID-19 is … a tricky thing to reason about — very tricky thing to reason about. Intuition breaks down,” said Jeremy Howard in an interview with VentureBeat. Howard is the cofounder of Fast.ai, which offers free courses on deep learning, and he is on the faculty at the University of San Francisco. He pointed out that there’s a long gap between an outbreak occurring and the visible results. The nature of an illness is that it takes a while for the disease to show itself in a person, and it takes longer to see it at scale.
“This is a perfect storm of what the human brain is bad at,” said Howard. “We respond to what we can see. And we respond to stories. A pandemic doesn’t give you those things.”
He continued, “But what we do have is data. Data scientists are people who know how to look at data and find out what story it’s telling us.” He joked that data scientists aren’t very good at telling the story — except through visualizations like those illustrating the need to “flatten the curve.”
2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 2.1182, Raw Interest Score: 1.0901,
Positive Sentiment: 0.1689, Negative Sentiment 0.3071
How A Data Scientist Can Effectively Network While Working From Home
With COVID-19 impacting daily lives and disrupting industries around the world, a major setback is being witnessed by data scientists in terms of the networking they indulge in, to help the data science community move forward. With every major conference and meetups being postponed or cancelled at the moment, the chances of networking with new people coming into the community and discussing different topics have come to a stop. However, networking is a crucial aspect for any data scientist as it is imperative to remain highly active in the community. Due to the above-mentioned reason, data scien…
2020-03-19 10:30:59+00:00 Read the full story…
Weighted Interest Score: 2.0206, Raw Interest Score: 1.0945,
Positive Sentiment: 0.2526, Negative Sentiment 0.2315
Coronavirus: the data behind the disease
In mid-January, China launched an official investigation into a string of unusual pneumonia cases in Hubei province. Within two months, that cluster of cases would snowball into a full-blown pandemic, with hundreds of thousands — perhaps even millions — of infections worldwide, with the potential to unleash a wave of economic damage not seen since the 1918 Spanish influenza or the Great Depression.
The exponential growth that led us from a few isolated infections to where we are today is profoundly counterintuitive. And it poses many challenges for the epidemiologists who need to pin down the transmission characteristics of the coronavirus, and for the policy makers who must act on their recommendations, and convince a generally complacent public to implement life-saving social distancing measures.
With the coronas in full bloom, I thought now would be a great time to reach out to Jeremy Howard, co-founder of the incredibly popular Fast.ai machine learning education site. Along with his co-founder Rachel Thomas, Jeremy authored a now-viral report outlining a data-driven case for concern regarding the coronavirus.
2020-03-20 17:00:16.174000+00:00 Read the full story…
Weighted Interest Score: 1.9709, Raw Interest Score: 0.9405,
Positive Sentiment: 0.2427, Negative Sentiment 0.4551
Top 20 Data Science YouTube Channels you should subscribe to in 2020
Here are the best YouTubers you should follow to learn about programming, Machine learning and AI, mathematics and Data Science.
YouTube is a great platform for both entertainment and education. The best thing about it is, there isn’t a 10 dollar per month subscription to watching videos on Youtube, it’s all free for you to watch. Except for the only currency, you pay for watching them is your time, and what you decide to watch is entirely up to you….
2020-03-23 05:49:19.304000+00:00 Read the full story…
Weighted Interest Score: 1.8913, Raw Interest Score: 1.0887,
Positive Sentiment: 0.5645, Negative Sentiment 0.1613
Can AI bots lend a virtual hand in the virus crisis?
“It’s not us looking for more business. It’s us trying to speed up processes to help,” Adrian Jones, Automation Anywhere’s regional executive vice president, told the The Australian Financial Review.
Before it was a cost saving initiative. Now it’s arguably a life-saving initiative. — Hanno Blankenstein
“We’ve had many inbound approaches saying ‘Hey, can we somehow use video to manage the crisis,” said Hanno Blankenstein, Unleash Live chief exe…
2020-03-19 00:00:00 Read the full story…
Weighted Interest Score: 1.8400, Raw Interest Score: 0.8805,
Positive Sentiment: 0.0267, Negative Sentiment 0.1868
My Python Pandas Cheat Sheet
A mentor once told me that software engineers are like indexes not textbooks; we don’t memorize everything but we know how to look it up quickly. Being able to look up and use functions fast allows us to achieve a certain flow when writing code. So I’ve created this cheatsheet of functions I use everyday building web apps and machine learning models. This is not a comprehensive list but contains the functions I use most, an example, and my incites as to when it’s most useful.
- Viewing and Inspecting
- Adding / Dropping
2020-03-22 23:53:14.462000+00:00 Read the full story…
Weighted Interest Score: 1.7990, Raw Interest Score: 1.0318,
Positive Sentiment: 0.0688, Negative Sentiment 0.0000
Convolutional Neural Networks With Heterogeneous Metadata
In autonomous driving, convolutional neural networks are the go-to tool for various perception tasks. Although CNNs are great at distilling information from camera images (or a sequence of them in form of a video clip), I constantly bump into all kinds of metadata that do not lend themselves to convolutional neural networks.
Metadata, by traditional definition, means a set of data used to describe other data. Here in this post, by metadata, we mean:
- heterogeneous, unstructured or unordered data that accompanies camera image data as auxiliary information. In the sense of the traditional definition, these data “describe” the camera data.
- The size of metadata is usually much less than camera image data, ranging from a few to at most a few hundred numbers per image.
- And unlike image data, metadata cannot be represented by a regular grid, and the length of metadata per image may not be constant.
All these properties make it hard for CNN to consume the metadata directly as CNN assumes a data representation on a regular-spaced grid, and neighboring data on the grid has a closer spatial or semantic relationship as well.
2020-03-18 16:40:02+00:00 Read the full story…
Weighted Interest Score: 1.7859, Raw Interest Score: 0.9985,
Positive Sentiment: 0.0742, Negative Sentiment 0.1027
Banks, Finance IT Hiring Technologists Despite COVID-19 Crunch
In London, on Wall Street, in Paris and in Frankfurt, COVID-19 has unleashed some crazy times. Things that were taken for granted last month (or even last week) no longer hold as societies go into lockdown and markets crash. This isn’t likely to quickly pass: this week’s COVID-19 modelling document from London’s Imperial College suggests that if social distancing measures and quarantines are maintained until August and then lifted, there will simply be an even bigger surge in the virus in the coming autumn and winter.
What does all of this mean if you were hoping to change jobs this year? With health considerations to the fore, some of you will shelve plans to find new roles.
But as we noted last week, banks are still hiring despite the COVID-19 threat. New jobs continue to be released and candidates continue to be interviewed… albeit by video and telephone rather than face-to-face. However, hiring right now is far more likely in the middle and back office than in the front, even though this is traditionally front office hiring season for finance.
2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 1.6468, Raw Interest Score: 1.1467,
Positive Sentiment: 0.2150, Negative Sentiment 0.2389
What Could a Future of AI-augmented Infectious Disease Surveillance Look Like?
What Could a Future of AI-augmented Infectious Disease Surveillance Look Like? And how close are we to this today?
Before the Covid-19 pandemic, an estimated 40 million flights, carrying almost 5 billion people were estimated to operate globally in 2020. This number is growing year upon year, meaning that if a new infectious strain of a disease emerges, it can spread locally and internationally at a speed and scale not seen in the past.
Despite enormous technological advances over the past few centuries, infectious diseases still threaten global health. The possibility of the rapid, unexpected spread of an infectious agent across the world has become much higher, due to increasing globalisation. Rapid urbanization, an increase in international travel and trade, and the modification of agriculture and environmental changes have increased the spread of vector populations, putting more people at risk.
2020-03-23 09:20:11.742000+00:00 Read the full story…
Weighted Interest Score: 1.5925, Raw Interest Score: 0.8964,
Positive Sentiment: 0.1358, Negative Sentiment 0.3350
Most In-Demand Skills, February and March 2020: Python, SQL, and More
That should come as no surprise, of course, since companies not only rely on these languages to build new apps and services—they also need technologists who can maintain mountains of legacy code. (In this respect, the Burning Glass list also echoes the most-popular-languages lists that update periodically, such as TIOBE.) The following chart represents tech skills that popped up in open job postings between February 18 and March 18, 2020:
2020-03-20 00:00:00 Read the full story…
Weighted Interest Score: 1.4406, Raw Interest Score: 0.9610,
Positive Sentiment: 0.1502, Negative Sentiment 0.1502
The Growing Importance of Customer Data Mining for SMEs
Every SME needs to get the most value of their customer data. They can find that this will significantly increase the ROI of their marketing campaigns.
Big data is changing the direction of small and medium sized businesses. They can use big data for many purposes. However, the value of their big data strategies will vary considerably. Using big data to get a better understanding of your customers is important. Wired author Mike Dickey has written a great article on 10 ways that big data can be used to get a more thorough understanding of your customers. Unfortunately, not all SMEs use this data effectively. There are two factors that affect the value of a company’s SME customer big data strategy:
- The quality and volume of the customer data the organization has acquired
- The strategy the company employs for leveraging this data
Customer data can be very valuable, but it needs to be utilized effectively. Fortunately, there are a number of ways companies can make the most of their customer data.
2020-03-18 20:16:37+00:00 Read the full story…
Weighted Interest Score: 1.4026, Raw Interest Score: 0.7683,
Positive Sentiment: 0.3415, Negative Sentiment 0.1341
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