Once you start consuming machine learning content such as books, articles, video courses, and blog posts, you will often see the terms like artificial intelligence, machine learning, deep learning, big data, and data science being used interchangeably. These terms represent several closely related areas within the field of artificial intelligence. They are usually used interchangeably without adequate attention paid to their scopes. It’s not entirely the authors’ fault since there is a slight ambiguity about these terms’ differences. With this post, we will put an end to this ambiguity and clarify their scopes, covering: Artificial Intelligence, Machine Learning. Deep Learning, Data Science, and Big Data. Read More
Monthly Archives: December 2020
NLP: Gaining insights from text reviews
A look at various NLP tasks one can perform on text reviews to extract different kinds of information using machine learning
Every time we express ourselves, either verbally or in written text, these expressions carry a lot of information. What subjects we talk about, if we express opinions or facts, our selection of words etc., all of which add some kind of information to our expressions, and can be interpreted and extracted to gain insights.
With all the web content in terms of e.g. consumer reviews and social media posts we have today, companies now have access to tons of useful information that can help improve their business. In most cases however, the huge amount of data available is not manageable to process manually, and we thus need a way to automate this process. This leads us in on the field of machine learning called Natural Language Processing, or NLP Read More
2020: A Big Data Year in Review
There are two weeks left in 2020, which means it’s time to exhale a bit and see where we’ve gone. It’s been a bumpy ride over the previous 50 weeks, for sure. But big strides have also been made for those pursuing big data, advanced analytics, and AI, and those accomplishments deserve some credit.
The big story of 2020, of course, was COVID-19. …The other major story of the year was the rise of the public cloud. The cloud was already growing fast at the beginning of 2020–and then COVID-19 happened and cloud growth kicked into overdrive. AWS grew at a 33% rate in the first quarter ended March 31, Google Cloud at 34%, and Microsoft Azure at a whopping 59%. Amazon.com, meanwhile, went on a hiring binge, bolstering its employee rolls by 380,000 over the past year. Collectively, Amazon, Microsoft, and Google their collective market capitalizations from $3.07 trillion to $4.43 trillion since December 30, 2019. (Apple, which does not run its own cloud, grew its market cap by nearly a trillion dollars.) Read More
Machine Learning 2020 summary: 84 interesting papers/articles
This article presents a total of 84 papers and articles published in 2020 that the author found particularly interesting. For the sake of clarity, he divides them into 12 sections, including a personal summary:
1.Image/video classification tasks
2.Unsupervised learning / self-supervised learning
3.Natural language processing
4.Sparse model / Model compression / inference speedup
5.Optimization/ loss function/ data augmentations
6.Deep fake
7.Generative models
8.Machine learning with natural sciences
9.Analysis of deep learning
10.Other research
11.Real world applications
Read More
Top 100 Artificial Intelligence Companies in the World
Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. Millions of users interact with AI directly or indirectly on a day-to-day basis via virtual assistants, facial recognition technology, gaming platforms, chatbots, mapping applications and a host of other software. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence.
As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. Please note: The companies are in Alphabetical order with no internal rankings Read More
In a first, Air Force uses AI on military jet
Defense officials touted the test as a watershed moment for a technology intensely debated in aviation and arms control communities
The Air Force allowed an artificial-intelligence algorithm to control sensor and navigation systems on a U-2 Dragon Lady spy plane in a training flight Tuesday, officials said, marking what is believed to be the first known use of AI onboard a U.S. military aircraft.
No weapons were involved, and the plane was steered by a pilot. Even so, senior defense officials touted the test as a watershed moment in the Defense Department’s attempts to incorporate AI into military aircraft, a subject that is of intense debate in aviation and arms control communities. Read More
Honey I Shrunk the Model: Why Big Machine Learning Models Must Go Small
Bigger is not always better for machine learning. Yet, deep learning models and the datasets on which they’re trained keep expanding, as researchers race to outdo one another while chasing state-of-the-art benchmarks. However groundbreaking they are, the consequences of bigger models are severe for both budgets and the environment alike. For example, GPT-3, this summer’s massive, buzzworthy model for natural language processing, reportedly cost $12 million to train. What’s worse, UMass Amherst researchers found that the computing power required to train a large AI model can produce over 600,000 pounds of CO2 emissions – that’s five times the amount of the typical car over its lifespan.
At the pace the machine learning industry is moving today, there are no signs of these compute-intensive efforts slowing down. Research from OpenAI showed that between 2012 and 2018, computing power for deep learning models grew a shocking 300,000x, outpacing Moore’s Law. The problem lies not only in training these algorithms, but also running them in production, or the inference phase. For many teams, practical use of deep learning models remains out of reach, due to sheer cost and resource constraints. Read More
Top 20 Websites for Machine Learning and Data Science in 2020
Data science is booming exponentially in almost all parts of the world. Data scientists are highly sought after because they seem to have the “magical” ability to create value from data for data-driven companies and organizations.
Here is a list of the best websites for ML and data science to follow for valuable resources and news.
1 — Machine Learning Mastery
2 — Elite data science
3 — KDnuggets
4 — Kaggle
5 — Reddit — r/datascience
6 — Towards Data Science
7 — Analytics Vidhya
8 — Data Science Dojo
9 — Data Science 101
10 — Geeks for Geeks — Machine Learning
11 — Google News — Data Science
12 — Datafloq
13 — Domino Data Science Blog
14 — data36
15 — Revolutions
16 — Edwin Chen
17 — Pete Warden’s Blog
18 — InsideBIGDATA
19 — Google AI Blog
20 — Nature
Read More
Getting the Future Right: Artificial Intelligence and Fundamental Rights
Developments in AI have received wide attention by the media, civil society, academia, human rights bodies and policymakers. Much of that attention focuses on its potential to support economic growth. How different technologies can affect fundamental rights has received less attention. To date, we do not yet have a large body of empirical evidence about the wide range of rights AI implicates, or about the safeguards needed to ensure that the use of AI complies with fundamental rights in practice.
On 19 February 2020, the European Commission published a White Paper on Artificial Intelligence – A European approach to excellence and trust. It outlines the main principles of a future EU regulatory framework for AI in Europe. The White Paper notes that it is vital that such a framework is grounded in the EU’s fundamental values, including respect for human rights – Article 2 of the Treaty on European Union (TEU).
This report supports that goal by analysing fundamental rights implications when using artificial intelligence. Based on concrete ‘use cases’ of AI in selected areas, it focuses on the situation on the ground in terms of fundamental rights challenges and opportunities when using AI. Read More
Ensemble Learning: Stacking, Blending & Voting
If you want to increase the effectiveness of your ML model, maybe you should consider Ensemble Learning
We have heard the phrase “unity is strength”, whose meaning can be transferred to different areas of life. Sometimes correct answers to a specific problem are supported by several sources and not just one. This is what Ensemble Learning tries to do, that is, to put together a group of ML models to improve solutions to specific problems. Read More
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