Industry News

Industry News

AI ML News January 2021 : Graphcore raises $222 million to scale up AI chip production : Generate Your ML Code In Few Clicks Using Train Generator : Google Move Into Healthcare Leveraging its AI Getting More Pronounced : How To Leverage GPUs For Recommendation Engines At Scale : AI Autonomous Cars Might Not Know They Were In A Car Crash : NLP featuring heavily in recent top news articles

AI & Machine Learning News. 05, January 2021

AI & Machine Learning News. 05, January 2021

AI and Machine Learning Newsletter

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?

Graphcore raises $222 million to scale up AI chip production

Graphcore, a Bristol, U.K.-based startup developing chips and systems to accelerate AI workloads, today announced it has raised $222 million in a series E funding round led by the Ontario Teachers’ Pension Plan Board. The investment, which values the company at $2.77 billion post-money and brings its total raised to date to $710 million, will be used to support continued global expansion and further accelerate future silicon, systems, and software development, a spokesperson told VentureBeat.
The AI accelerators Graphcore is developing — which the company calls Intelligence Processing Units (IPUs) — are a type of specialized hardware designed to speed up AI applications, particularly neural networks, deep learning, and machine learning. They’re multicore in design and focus on low-precision arithmetic or in-memory computing, both of which can boost the performance of large AI algorithms and lead to state-of-the-art results in natural language processing, computer vision, and other domains.

2020-12-29 00:00:00 Read the full story (VentureBeat)…
2020-12-29 00:00:00 Read the full story (CNBC)…
Weighted Interest Score: 3.1552, Raw Interest Score: 1.5949,
Positive Sentiment: 0.1345, Negative Sentiment 0.0576

CloudQuant Thoughts : Things have gone quiet recently in the field of AI Specific chipsets.. this IPU idea from NVidia challenger Graphcore could be the next big thing!

Generate Your ML Code In Few Clicks Using Train Generator

TrainGenerator is a Streamlit based web app for machine learning template code generation surpassing the different stages of data loading, preprocessing, model development, hyperparameter setting, and declaring other such constraints for complete model building. This wonderful open-source software has been created by Johannes Rieke, a machine learning engineer. This eases the task of data scientists and also non-technical people in the field of data science and machine learning. The code can then be used in Google Colab notebook or downloaded in .py or .ipynb formats.
2021-01-04 12:00:00+00:00 Read the full story…
Weighted Interest Score: 3.0511, Raw Interest Score: 1.6475,
Positive Sentiment: 0.0553, Negative Sentiment 0.2764

CloudQuant Thoughts : Anything that helps speed up ML Code Generation gets a thumbs up here!

Google Move Into Healthcare Leveraging its AI Getting More Pronounced

Google is making a more pronounced move into healthcare, leveraging its power to acquire companies and to use AI technology to disrupt the industry.
From an investment point of view, the company’s move has attracted attention. Google now has 57 digital health startups in its portfolio, according to a recent account in The Motley Fool. While the November 2019 acquisition of Fitbit generated headlines, “its investments and partnerships with healthcare service providers are more likely to be the gateway to the next big thing,” the authors stated.
The company’s investments have been focused on improving electronic health records (EHRs), diagnostic capabilities, bundling healthcare services in the cloud, and leveraging its AI expertise to advance scientific research.

2020-12-29 17:02:58+00:00 Read the full story…
Weighted Interest Score: 2.6181, Raw Interest Score: 1.5513,
Positive Sentiment: 0.1756, Negative Sentiment 0.1317

CloudQuant Thoughts : Cloud Healthcare API, Fitbit.. Google is making moves!

How To Leverage GPUs For Recommendation Engines At Scale

The majority of deep learning recommendation models are trained on CPU servers, unlike language models, which are trained on GPU systems.
sing GPUs at scale comes with various challenges due to compute-intensive and memory-intensive components. For instance, GPUs that train state-of-the-art personal recommendation models are largely affected by model architecture configurations such as dense and sparse features or dimensions of a neural network. These models often contain large embedding tables that do not fit into limited GPU memory.
The majority of deep learning recommendation models are trained on CPU servers, unlike language models, which are trained on GPU systems. This is because of the large memory capacity and bandwidth requirement of embedding tables in these models. The memory capacity of embedding tables has increased dramatically from tens of GBs to TBs throughout the industry. At the same time, memory bandwidth usage also increased quickly with the increasing number of embedding tables and the associated lookups.
According to reports, over the last 18-month period, the compute capacity for recommendation model training quadrupled at Facebook’s data center fleet. Among the total AI training cycles at Facebook, more than 50% has been devoted to training deep learning recommendation models.

2021-01-04 08:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5554, Raw Interest Score: 1.6426,
Positive Sentiment: 0.1705, Negative Sentiment 0.1240

CloudQuant Thoughts : I love little throwaway stats like that last sentence.. .”Among the total AI training cycles at Facebook, more than 50% has been devoted to training deep learning recommendation models.”!!

AI Autonomous Cars Might Not Know They Were In A Car Crash

Will an AI-based true self-driving car be able to discern that it has been in a car crash, and what does this foretell about the advent of self-driving cars?
The reason this is worthwhile to point out is that unlike a human driver that can “feel” a car crash, there isn’t a robotic body that sits in the vehicle and equally ascertains the sensations that arise when a crash occurs (though, via the IMU and the potential addition of other special tactile related sensors, this might be possible to detect).
Nor smells of the car crash (well, just a heads-up, some are adding e-nose features into their self-driving cars, see my coverage on the topic).
We’ll get in a moment to seeing or hearing the crash.
In theory, a self-driving car could get sideswiped by another car, for example, and the AI might be oblivious that this kind of car collision has even occurred.

2020-12-29 14:19:18+00:00 Read the full story…
Weighted Interest Score: 1.7066, Raw Interest Score: 0.5655,
Positive Sentiment: 0.0604, Negative Sentiment 0.3669

CloudQuant Thoughts : Dr Lance Elliot’s posts on AI Autonomous Cars are always interesting, as a regular reader/listener (he has a podcast) I do wish he would direct people elsewhere for his summary of different levels of Automation. This one, particularly the comment about sense of smell, knowing that your car is filling with gas fumes, was a very interesting point that I had not considered.

NLP featuring heavily in recent top news articles…

Top Rated MOOCs For Learning Natural Language Processing

Natural Language Processing (NLP) has made several ground-breaking achievements in the past couple of years. In the current scenario, almost all organisations use this technique to bring about human-like conversation capabilities in machines, among other applications.
As the concept’s popularity is growing, many courses are offering machine learning enthusiasts to take a deep dive and understand this technique from scratch. Here we list eight top-rated Natural Language Processing (NLP) MOOCs to learn the concepts from.
Note: The list is in no particular order

2020-12-31 06:30:00+00:00 Read the full story…
Weighted Interest Score: 4.7527, Raw Interest Score: 2.3905,
Positive Sentiment: 0.0912, Negative Sentiment 0.0912

What is Natural Language Processing (NLP)?

Natural language processing (NLP) describes a branch of artificial intelligence (AI) that automates language recognition and generation so that computers and humans can communicate seamlessly. To interact with humans, computers must be adept at and understand syntax (grammar), semantics (word meaning), morphology (tense), and pragmatics (conversation). These tasks have proven quite complex.
Natural language processing encompasses machine learning tactics needed to process intricate transactions, including, among others, the following: Computational Linguistics, Graphic Processing Units (GPUs), and Advanced Digital Neural Networks.

2020-12-31 08:30:16+00:00 Read the full story…
Weighted Interest Score: 3.8769, Raw Interest Score: 2.6005,
Positive Sentiment: 0.1576, Negative Sentiment 0.0788

Top 7 NLP Trends To Look Forward To In 2021

Natural language processing first studied in the 1950s, is one of the most dynamic and exciting fields of artificial intelligence. With the rise in technologies such as chatbots, voice assistants, and translators, NLP has continued to show some very encouraging developments. In this article, we attempt to predict what NLP trends will look like in the future as near as 2021.

Sentiment Analysis On Social Media

A large amount of data is generated at every moment on social media. It also births a peculiar problem of making se…
2021-01-01 07:30:00+00:00 Read the full story…
Weighted Interest Score: 3.7073, Raw Interest Score: 2.1233,
Positive Sentiment: 0.2757, Negative Sentiment 0.1792

Startup: Truera Raising Money to Get AI Explainability Solution to Market

In the black box problem in machine learning, data goes in, suggested decisions come out, and how the model arrived at its suggestions may or may not be explainable. This problem intrigued Prof. Anupam Datta of Carnegie Mellon University, who in 2014 with his PhD student Shayak Sen began researching explainable AI. At the same time, Will Uppington was among the founders at Bloomreach, which was trying to make black box machine learning models into a commercial product, and was running into similar issues around visibility into how models produce their answers.
2020-12-29 16:00:14+00:00 Read the full story…
Weighted Interest Score: 5.0205, Raw Interest Score: 2.4117,
Positive Sentiment: 0.2837, Negative Sentiment 0.2995

Meet AutoGL: The First Ever AutoML Framework for Graph Datasets

Researchers at Tsinghua University recently released an autoML framework and toolkit for machine learning on graphs, known as AutoGL. AutoGL version 0.1.1 is claimed to be the first-ever autoML toolkit for graph datasets and tasks.

AutoML or automated machine learning has gained much traction over the years. It helps in bridging the talent gap in the machine learning industry. On the other hand, graphs are the ubiquitous data structure that various researchers have thoroughly applied in their work. As this new toolkit supports the fully automatic machine learning of graph data, it will help eliminate the mundane tasks of machine learning developers.
2020-12-31 05:30:00+00:00 Read the full story…
Weighted Interest Score: 4.5816, Raw Interest Score: 2.0442,
Positive Sentiment: 0.2585, Negative Sentiment 0.0235

You don’t code? Do machine learning straight from Microsoft Excel

Machine learning and deep learning have become an important part of many applications we use every day. There are few domains that the fast expansion of machine learning hasn’t touched. Many businesses have thrived by developing the right strategy to integrate machine learning algorithms into their operations and processes. Others have lost ground to competitors after ignoring the undeniable advances in artificial intelligence.
But mastering machine learning is a difficult process. You need to start with a solid knowledge of linear algebra and calculus, master a programming language such as Python, and become proficient with data science and machine learning libraries such as Numpy, Scikit-learn, TensorFlow, and PyTorch.
And if you want to create machine learning systems that integrate and scale, you’ll have to learn cloud platforms such as Amazon AWS, Microsoft Azure, and Google Cloud.
Naturally, not everyone needs to become a machine learning engineer. But almost everyone who is running a business or organization that systematically collects and processes can benefit from some knowledge of data science and machine learning. Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.
But in my experience, a good understanding of data science and machine learning requires some hands-on experience with algorithms. In this regard, a very valuable and often-overlooked tool is Microsoft Excel.

2020-12-30 00:00:00 Read the full story…
Weighted Interest Score: 4.4521, Raw Interest Score: 2.2953,
Positive Sentiment: 0.2117, Negative Sentiment 0.1783

Truera Receives $12 Million in Latest Funding Round

Truera, providers of a Model Intelligence platform, is closing in on $12 million in Series A funding that will accelerate recruiting, product development, and sales and marketing.

The round was led by Wing VC with participation from returning investors Conversion Capital and Greylock and new investors Data Community Fund, B Capital Group via the firm’s Ascent Fund, and Harpoon Ventures. This brings Truera’s total funding to date to $17.3M.

Truera’s model intelligence software removes the “black box” surrounding Machine Learning (ML) and provides intelligence and actionable insights throughout the ML model lifecycle—enabling companies to improve the quality and accuracy of their models, boost stakeholder collaboration, and address responsible AI concerns including explainability and bias.
2020-12-28 00:00:00 Read the full story…
Weighted Interest Score: 4.4215, Raw Interest Score: 2.3160,
Positive Sentiment: 0.2895, Negative Sentiment 0.1654

Amazon, we don’t need another AI tool or APl, we need an open AI platform for cloud and edge

After Amazon’s three-week re:Invent conference, companies building AI applications may have the impression that AWS is the only game in town. Amazon announced improvements to SageMaker, its machine learning (ML) workflow service, and to Edge Manager — improving AWS’ ML capabilities on the edge at a time when serving the edge is considered increasingly critical for enterprises. Moreover, the company touted big customers like Lyft and Intuit.
But Mohammed Farooq believes there is a better alternative to the Amazon hegemon: an open AI platform that doesn’t have any hooks back to the Amazon cloud. Until earlier this year, Farooq led IBM’s Hybrid multi-cloud strategy, but he recently left to join the enterprise AI company Hypergiant.

2020-12-31 00:00:00 Read the full story…
Weighted Interest Score: 4.3018, Raw Interest Score: 1.6579,
Positive Sentiment: 0.2580, Negative Sentiment 0.1032

Leading computer scientists debate the next steps for AI in 2021

The 2010s were huge for artificial intelligence, thanks to advances in deep learning, a branch of AI that has become feasible because of the growing capacity to collect, store, and process large amounts of data. Today, deep learning is not just a topic of scientific research but also a key component of many everyday applications. But a decade’s worth of research and application has made it clear that in its current state, deep learning is not the final solution to solving the ever-elusive challenge of creating human-level AI.
What do we need to push AI to the next level? More data and larger neural networks? New deep learning algorithms? Approaches other than deep learning? This is a topic that has been hotly debated in the AI community and was the focus of an online discussion Montreal.AI held last week. Titled “AI debate 2: Moving AI forward: An interdisciplinary approach,” the debate was attended by scientists from a range of backgrounds and disciplines.

2021-01-02 00:00:00 Read the full story…
Weighted Interest Score: 4.2043, Raw Interest Score: 2.1627,
Positive Sentiment: 0.1399, Negative Sentiment 0.1829

WEF Releases Ethics by Design Report as a Guide to Responsible AI

The World Economic Forum (WEF) has released “Ethics by Design—An Organizational Approach to the Responsible Use of Technology,” a report detailing steps and recommendations for achieving ethical use of technology. “Ethics will be crucial to the success of the Fourth Industrial Revolution. The ethical challenges will only continue to grow and become more prevalent as machines advance. Organizations across industries—both private and public—will need to integrate these approaches.” stated WEF’s Head of Artificial Intelligence and Machine Learning Kay Firth-Butterfield in a press release.
The report recommends that a comprehensive approach to fostering organization ethics around AI should include three components: Attention, Construal, and Motivation.

2020-12-29 15:01:36+00:00 Read the full story…
Weighted Interest Score: 3.9071, Raw Interest Score: 1.4261,
Positive Sentiment: 0.2245, Negative Sentiment 0.2773

AI jobs in 2021: here are some key trends

There’s no doubt about it – Artificial Intelligence has been a bit of a buzzword this year. Artificial intelligence has been established as the main driver of emerging technologies such as big data, robotics, and the IoT. So, what do the next 12 months look like for AI?
As a result of the global pandemic, consumer trends have changed significantly, which has resulted in some notable trends in the world of AI for 2021 : Hyperautomation, Ethical AI , Workplace AI, and Cybersecurity.

2020-12-28 00:00:00 Read the full story…
Weighted Interest Score: 3.5888, Raw Interest Score: 1.7636,
Positive Sentiment: 0.1876, Negative Sentiment 0.2627

20 Data Science Buzzwords and What They Really Mean

Choose your words wisely! Unlock the full potential of data science by adjusting phrases to the end-user

As a data scientist in a Tier One Consultancy, I crave unlocking value for clients. My role is to bring value by applying core data science techniques. Doing so, I sometimes face cases where data science solutions are excluded. The techniques are simple still referred to as black-box methods. This might at times be true but often it is a result of the lack of effective communication. We have failed as data scientists when people see every method as black-box methods.  The field of data science should be for everyone. It is our job to communicate core techniques and results for everyone to understand.

Historically, technical departments primarily served as a support function and help desk. They were in big need when having technical issues or problems but not a part of the team. As a data scientist, you can only perform if included in the full process. Here the siloed mentality is no longer ideal. Let’s end the era of siloed processes together. By respecting the end user we can be more inclusive to non-technical people. This will unlock the full potential of data science and analytics!

2021-01-04 13:21:50.723000+00:00 Read the full story…
Weighted Interest Score: 3.3604, Raw Interest Score: 1.8714,
Positive Sentiment: 0.4611, Negative Sentiment 0.1356

Skype co-founder Jaan Tallinn reveals the 3 existential risks he’s most concerned about

Skype co-founder Jaan Tallinn said artificial intelligence, synthetic biology and so-called unknown unknowns each represent an existential risk through to 2100. The entrepreneur turned investor sees them as the three biggest threats to humanity’s existence this century. “Climate change is not going to be an existential risk unless there’s a runaway scenario,” said Tallinn.
While the climate emergency and the coronavirus pandemic are seen as issues that require urgent global solutions, Tallinn told CNBC that artificial intelligence, synthetic biology and so-called unknown unknowns each represent an existential risk through to 2100.
2020-12-29 00:00:00 Read the full story…
Weighted Interest Score: 3.3072, Raw Interest Score: 1.2118,
Positive Sentiment: 0.1515, Negative Sentiment 0.3282

Complete Conversational AI Solution Keeps Eluding Financial Institutions

As COVID-19 concerns continue to form a barrier between retail bankers and the consumers they serve, something better must be found to supplant chatbots that people only trust to handle everyday financial matters. A blend of video, audio, text, human and artificial intelligence may be the answer.
Conversational artificial intelligence has gotten a lot of buzz lately, and for good reason. As lockdowns closed financial institution branches and pushed banking online, usage of chatbots and virtual agents soared.
Bank of America’s AI financial assistant “Erica,” for example, gained one million users from March through May. A free chatbot available in the BofA app, Erica, uses predictive analytics and natural language to provide account balances, execute transfers, send money over Zelle, and even schedule meetings with financial advisors. The AI bot communicates with customers via voice, text or through tappable prompts that appear on a mobile phone’s screen.
2020-12-28 00:01:24+00:00 Read the full story…
Weighted Interest Score: 3.2870, Raw Interest Score: 1.3119,
Positive Sentiment: 0.2315, Negative Sentiment 0.1852

2021 Predictions: Rise of ‘Glocalization,’ Model Monitoring, Focus on Supply Chain

We have heard from a range of AI practitioners for their predictions on AI Trends in 2021. Here are predictions from a selection of those writing.

2020-12-29 15:11:50+00:00 Read the full story…
Weighted Interest Score: 3.0930, Raw Interest Score: 1.3683,
Positive Sentiment: 0.2046, Negative Sentiment 0.2558

Top 5 Inductive Biases In Deep Learning Models

The learning algorithms mostly use some mechanisms or assumptions by either putting some restrictions on the space of hypotheses or can be said as the underlying model space. This mechanism is known as the Inductive Bias or Learning Bias.
This mechanism encourages the learning algorithms to prioritise solutions with specific properties. In simple words, learning bias or inductive bias is a set of implicit or explicit assumptions made by the machine learning algorithms to generalise a set of training data.
Here, we have compiled a list of five interesting inductive biases, in no particular order, which are used in deep learning.

2020-12-30 12:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9251, Raw Interest Score: 1.7131,
Positive Sentiment: 0.2141, Negative Sentiment 0.0476

Peering Into the Crystal Ball of Advanced Analytics

The world of advanced analytics was evolving quickly at the end of 2020. And according to our panel of experts who volunteered predictions on the topic, the accelerated pace of change in advanced data analytics will continue in 2021.

2021 kicks off a new decade for advanced analytics, and a new attitude is apparent. GoodData CEO Roman Stanek, for one, is bullish on the potential.

The 2010s were the ‘Lost Decade’ for data, in large part due to Silicon Valley’s misplaced obsession with Hadoop,” Stanek tells Datanami. “The 2020s, in contrast, will be data’s ‘Decade of Growth.’ Snowflake captured an entire cloud data market and will change the data landscape as we know it. Standardized cloud storage will redefine data management and the data value chain. The result? Massive growth and the software industry’s first $100 billion IPO.”
2021-01-04 00:00:00 Read the full story…
Weighted Interest Score: 2.7961, Raw Interest Score: 1.4651,
Positive Sentiment: 0.2686, Negative Sentiment 0.1628

Social Cooling: Living in a Big Data Society

“Data is not the new gold, it is the new oil, and it damages the social environment,” stated Tijmen Schep. Strong words, indeed. But if you’ve been inundated with articles espousing all the wonders that Big Data brings over the last few years, then it’s high time for a wake-up call. To take Schep’s thoughts further, like oil leads to global warming, so data leads to social cooling. So why should any of us be worried about the concept of social cooling?
Big Data (indeed big companies and big industry) is largely getting out of control, with everything now being turned into data. Too much centralised power (responsibility) rests in the hands of a few private companies, without too much accountability insofar as how the data that they collect / hold is processed. The notion that Big Brother is watching you has never felt so absolute and the notion that many of us are changing our behaviour because of this intense scrutiny is worrying to say the least. Make no mistake, Big Data is supercharging this effect.
And we can’t talk about Big Data without illustrating the part that algorithms have to play in all of this mayhem. Essentially, anytime your data is collected and scored, so-called data brokers use algorithms to uncover all kinds of private details about you—friends and acquaintances, religious and political beliefs, and even sexual orientation or economic stability.

2020-12-28 00:00:00 Read the full story…
Weighted Interest Score: 2.7901, Raw Interest Score: 1.0532,
Positive Sentiment: 0.1109, Negative Sentiment 0.3695

The immense potential and challenges of multimodal AI

Unlike most AI systems, humans understand the meaning of text, videos, audio, and images together in context. For example, given text and an image that seem innocuous when considered apart (e.g., “Look how many people love you” and a picture of a barren desert), people recognize that these elements take on potentially hurtful connotations when they’re paired or juxtaposed.
While systems capable of making these multimodal inferences remain beyond reach, there’s been progress. New research over the past year has advanced the state-of-the-art in multimodal learning, particularly in the subfield of visual question answering (VQA), a computer vision task where a system is given a text-based question about an image and must infer the answer. As it turns out, multimodal learning can carry complementary information or trends, which often only become evident when they’re all included in the learning process. And this holds promise for applications from captioning to translating comic books into different languages.

2020-12-30 00:00:00 Read the full story…
Weighted Interest Score: 2.6283, Raw Interest Score: 1.2015,
Positive Sentiment: 0.2115, Negative Sentiment 0.2307

Expanding Your Data Science and Machine Learning Capabilities – Webinar registration

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. As a result, new solutions have emerged to deliver key capabilities in area…
2021-06-24 00:00:00 Read the full story…
Weighted Interest Score: 2.5974, Raw Interest Score: 1.6536,
Positive Sentiment: 0.2611, Negative Sentiment 0.1741

Unlocking the Power of DataOps

DataOps is on the rise at enterprises looking to bring improved quality and reduced cycle times to data analytics. Borrowing from Agile Development, DevOps and statistical process control, this new methodology is poised to revolutionize data analytics with its eye on the entire data lifecycle. 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 process changes as well as enabling…
2021-05-13 00:00:00 Read the full story…
Weighted Interest Score: 2.4819, Raw Interest Score: 1.4478,
Positive Sentiment: 0.7239, Negative Sentiment 0.1034

Industry Perspective: How AI is Revolutionizing Business Processes

Due to a number of different circumstances that have come together in the last half-dozen years to form an important convergence, artificial intelligence and machine learning are becoming more portable for use at the edge, in addition to their usual homes in the data center or in the cloud.
It’s all here now: high-speed bandwidth, 5G connectivity, super high-quality code and code libraries, unprecedentedly powerful processors that use less power than previous models, unlimited storage capacities, ingeniously designed mobile and stationary connected devices, a zillion types of cloud services–we could go on. What is next?
We’re already seeing it. the introduction of more functionality through artificial intelligence. We’re seeing more AI in more apps in more places than we’ve ever seen before: wearables, cars, productivity apps, military, health care, home entertainment–the list is lengthy.
This question-and-answer article is conducted with topic expert Vaibhav Nivargi, CTO and co-founder of Mountain View, Calif.-based Moveworks, which works on the front lines each day helping companies on their inclusion of AI for use in a wide range of IT use cases.

2020-12-29 00:00:00 Read the full story…
Weighted Interest Score: 2.4795, Raw Interest Score: 1.3076,
Positive Sentiment: 0.1219, Negative Sentiment 0.3214

Performance Metrics in ML – Part 2: Regression

Using the right performance metric for the right task
In the previous post of this three-part series, I went through the most common performance metrics that every Data Scientist should know when working on Classification tasks. (You can check the previous part of this series here.)
In the second part, I am going through the performance measures that are most applicable to Regression tasks. These are the most common tools to be able to effectively evaluate whether a model is actually well-performant and ready to be brought into Production or it still needs some

2021-01-04 13:47:31.164000+00:00 Read the full story…
Weighted Interest Score: 2.1953, Raw Interest Score: 1.3728,
Positive Sentiment: 0.1373, Negative Sentiment 0.6739

Data Management Best Practices for Machine Learning – Webinar Registration

Machine learning is on the rise at businesses hungry for greater automation and intelligence. A recent study fielded amongst the subscribers of DBTA found that 48% currently have machine learning initiatives underway with another 20% considering adoption. At the same time, most projects are still in the early phases. Machine learning is the new kid on the block. From data quality issues, to architecting and optim…
2021-01-21 00:00:00 Read the full story…
Weighted Interest Score: 2.1106, Raw Interest Score: 1.7224,
Positive Sentiment: 0.4053, Negative Sentiment 0.1013

Alphabet Workers Union launches with hundreds of members demanding change

“For far too long, thousands of us at Google — and other subsidiaries of Alphabet, Google’s parent company — have had our workplace concerns dismissed by executives,” the op-ed reads. “Our bosses have collaborated with repressive governments around the world. They have developed artificial intelligence technology for use by the Department of Defense and profited from ads by a hate group. They have failed to make the changes necessary to meaningfully address our retention issues with people of color.”

2021-01-04 00:00:00 Read the full story…
2021-01-04 00:00:00 Read the full story…
Weighted Interest Score: 2.0651, Raw Interest Score: 1.3776,
Positive Sentiment: 0.1252, Negative Sentiment 0.5322

P-value in a Nutshell: What Does it Actually Mean?

Understand, visualizing, and calculating p-value. Welcome to this lesson on calculating p-values. Before we jump into how to calculate a p-value, it’s important to think about what the p-value is really for.

Hypothesis Testing Refresher – Before we jump into how to calculate a p-value, it’s important to think about what the p-value is really for.
Without going into too much detail for this post, when establishing a hypothesis test, you will determine a null hypothesis. Your null hypothesis represents the world in which the two variables your assessing don’t have any given relationship. Conversely the alternative hypothesis represents the world where there is a statistically significant relationship such that you’re able to reject the null hypothesis in favor of the alternative hypothesis.

Diving Deeper – Before we move on from the idea of hypothesis testing… think about what we just said. You effectively need to prove that with little room for error, what we’re seeing in the real world could not be taking place in a world where these variables are not related or in a world where the relationship is independent.
2021-01-04 14:00:51.080000+00:00 Read the full story…
Weighted Interest Score: 2.0263, Raw Interest Score: 0.9333,
Positive Sentiment: 0.1474, Negative Sentiment 0.0982

3 Major Business Tech Trends You’ll See in 2021

Technology and business go hand in hand, and the two play off of each other spectacularly. While this has been the case since time immemorial, modern technology has reached a level of advancement that seems like pure sci fi. AI in particular has advanced to the point of seeing widespread use in the home and at the office, albeit to varying levels of success. Here are the business technology trends to look forward to in 2021 : Artificial Intelligence, Autonomous Vehicles, and Remote Employment.

2020-12-31 06:05:54+00:00 Read the full story…
Weighted Interest Score: 1.8007, Raw Interest Score: 1.0717,
Positive Sentiment: 0.3334, Negative Sentiment 0.0953

Data Science: I Wonder If It’s Still Exciting In 2021

The increase of data being produced each year was the provocative gesture that enlightened businesses to take more action and use the data at their disposal. Although data-driven decisions were around before 2012, a reputable write-up published in Havard Business Review attracted the attention of many.
“Their sudden appearance on the Business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before” — Havard Business Review, 2012.
With a title such as Data Scientists: The Sexiest Job of the 21st Century, It’s not difficult to understand why this piece may have been an influential factor in why many people want to become Data Scientists today. However, as we rapidly approach a decade since the original piece was published, I was curious to determine whether Data Science may have lost its sexiness over the years and whether it is a good career to pursue.

2021-01-04 13:19:46.896000+00:00 Read the full story…
Weighted Interest Score: 1.7839, Raw Interest Score: 1.0503,
Positive Sentiment: 0.3939, Negative Sentiment 0.2394

This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. We used natural language processing (NLP) to determine an interest score, and to calculate the sentiment of the linked article using the Loughran and McDonald Sentiment Word Lists.

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