AdobeStock_95325369

AI & Machine Learning News. 10, September 2018

Self-driving (very small) cars — Part I – Towards Data Science – Doing A.I. in the wild with Python and Bluetooth cars

A.I. meets the physical world (on a budget). “I’m one of those boys that appreciates a fine body, regardless of the make” – Dominic Toretto, The Fast and the Furious. Autonomous vehicle startups are so hot right now: even without considering the usual suspects (Uber, Waymo, Cruise, etc.), there is a ton of less known and relatively less funded startups attacking the space (there’s even a Udacity course!). The key word here is relatively: it does not matter how you do it, it is just a steep price to pay compared to your standard SaaS company (as cool tools like lidar are very expensive + collecting training/test data is a pain).

Why should these people have all the fun? Isn’t there anything we could do in our scrappy garage to start playing around? As it turns out, a common move in the industry is using simulators, from ad hoc physics engines to off-the-shelf video games (you can also try something in your browser!). However, we tried something else in our weekend hacking: instead of trading the physical world for a simulation of it, we chose to scale down the problem and work with (literally) a toy universe.
2018-09-09 22:11:31.214000+00:00 Read the full story.

CloudQuant Thoughts… Jacopo had an idea, using AI to control cars in the real world for a low price. He opted for a cheap track based toy car game only to discover that just getting the Bluetooth via SDK communication going was a hard task all to itself. In part two he promises to deliver some AI experiments using his python controlled real-world driving system.

This is so often the case with attacking a project, the initial chicanes prove way more difficult than you expected, but in the words of Randy Pausch, “the brick walls are there for a reason”. If you are like Jacopo and driven to succeed, why not apply that drive (instead of to tiny cars) to making real-world money in the stock market. We invite you python programmers to try CloudQuant and see if you can accelerate your wealth acquisition!

 

Chicago Crime Mapping: Magic of Data Science and Python

Predictions, Forecasts and Loss scores. Sound too mainstream, don’t they? In the era of increasing interest towards Machine Learning and its algorithms, we are hugely ignoring important duties of being a data scientist, and one of those is Data Exploration. We, the modern data scientists are so naive that we forget the beauty of Visualizations and the quality it stands for. Today, allow me to present you an Exploratory Data Analysis of the Kaggle Dataset: Crime in Chicago.


2018-09-08 17:42:24.033000+00:00 Read the full story.

CloudQuant Thoughts… As Uddeshya shows us, there is so much data out there that has simply not been looked at in enough ways. When we saw this story we were reminded of this post we saw on Reddit in the r/DataIsBeautiful subreddit. Even though the data is beautiful, the content is rather sad.

One year of accumulation of crime in central Chicago [OC] from dataisbeautiful

 

Failed AI Hedge Fund? Don’t Blame Artificial Intelligence, Blame the Program

I read the following piece from Bloomberg and all I could think was “DON’T BLAME THE AI”. Artificial intelligence is an incredible tool, and has myriads of applications both within and outside the trading realm. But it also has its limitations, inasmuch as it’s only as good as its’ programming. So it was with much fanfare that it was recently announced that an AI-based hedge fund, Sentient Investment Management, closed after less than 2 years, and after only earning 4% its first year.

It used algorithms to create the equivalent of trillions of “virtual” traders and was said to be able to squeeze 1800 trading days into a few minutes, while scouring its multiple computers for the best trading strategies. NOW MAYBE (or obviously), that wasn’t such a great strategy to try to create. It managed to demonstrate how many terrible trading strategies might be possible, so much so that they overwhelmed the returns.
2018-09-09 12:59:02-04:00 Read the full story.

CloudQuant Thoughts… “Before this year, the Eurekahedge AI Hedge Fund Index gained an average of 10.5 percent annually since its 2011 inception. This year, the measure of 15 funds is little changed…”. This story and the failure of Millenium’s Prediction Company demonstrate that, whilst AI and ML are definitely game changers in the financial industry and stock trading, they are not a magic pill. Human imagination should be the starting point for any strategy, right now these new technologies are working best when paired up with talented humans.

 


Below the Fold…

Apple veteran: ‘Fast fail’ won’t work with health tech

Think about the last piece of technology you bought that didn’t work as expected. What did you do? Return it? Give it away? Put it in a drawer with its sad digital cousins?

Most likely the stakes accompanying your poor experience were low, and you simply chalked it up to the cost of being an early adopter. What you didn’t do was abandon the field completely. If you were lucky enough to have spent your hard earned money on a Betamax, when that platform failed you didn’t swear off all forms of recorded entertainment. If you thought Chumby was the future of internet appliances, you haven’t refused to use an iPad or Alexa strictly on principle.
2018-09-06 00:00:00 Read the full story.

 

Trends Poised to Disrupt the Wealth Management Industry

Competition in the wealth management industry is increasing. Looming on the horizon is a significant evolution in how business is conducted. Big data and advanced analytics technologies are key elements driving transformation in operations, risk, and compliance. Incumbents are using advanced analytics to garner insight from historical data, forecast behavior patterns, and predict outcomes and emerging trends.

According to Boston Consulting Group, approximately 75% of wealth managers are planning to increase their use of big data and smart ana…
2018-09-06 04:34:49-04:00 Read the full story.

 

NASDAQ’s  Oliver Albers : The Growing Value of (Alternative) Data (Video)

“When I started in this business, data was very, very uncool. Trust me.”

Data is not uncool any longer. Consider that every minute there are more than 12 million texts sent and more than 4.3 million YouTube videos watched. It is estimated that humanity’s accumulated digital data haul will be more than 44 trillion gigabytes by 2020. There is even a band called Big Data.

2018-09-06 20:46:43+00:00 Read the full story.

 

AI Weekly: Contrary to current fears, AI will create jobs and grow GDP

The inevitable march toward automation continues, analysts from the McKinsey Global Institute and from Tata Communications wrote in separate reports this week.

Artificial intelligence’s growth comes as no surprise — a survey from Narrative Science and the National Business Research Institute conducted earlier this year found that 61 percent of businesses implemented AI in 2017, up from 38 percent in 2016 — but this week’s findings lay out in detail the likely socioeconomic impacts in the coming decade.
2018-09-07 00:00:00 Read the full story.

 

The Gaming Industry Is Revolutionising Artificial Intelligence, One Win At A Time

Today, artificial intelligence is dominating most of the games — from board games to interactive fiction games. They are providing complex, decision-making environments for AI to experiment with. The ability of games to provide interesting and complex problems, offering creativity and expression, has made them one of the most popular and meaningful domain for AI researchers.

Games offer one of the most meaningful domains that can process, interpret and stimulate human behaviour. The current gaming industry is not only deploying better graphics but is also exploring the area of virtual gameplay. The two-way relationship of gaming and AI has begun to tread a new road and it can be said that the gaming industry is largely revolutionising the way AI works.
2018-09-08 08:46:45+00:00 Read the full story.

 

How Do Machine Learning Algorithms Differ From Traditional Algorithms?

Machine learning is an algorithm or model that learns patterns in data and then predicts similar patterns in new data. For example, if you want to classify children’s books, it would mean that instead of setting up precise rules for what constitutes a children’s book, developers can feed the computer hundreds of examples of children’s books. The computer finds the patterns in these books and uses that pattern to identify future books in that category.

Essentially, ML is a subset of artificial intelligence that enables computers to learn without being explicitly programmed with predefined rules. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. This predictive ability, in addition to the computer’s ability to process massive amounts of data, enables ML to handle complex business situations with efficiency and accuracy.
2018-09-10 04:15:39+00:00 Read the full story.

 

Unanimous AI achieves 22% more accurate pneumonia diagnoses

It’s no great mystery that artificial intelligence’s (AI) predictive prowess can significantly improve health care outcomes. AI’s been shown to outperform dermatologists in diagnosing melanoma, and a recent study by Google subsidiary DeepMind found that algorithms were on par with clinicians in detecting eye conditions.

But what if AI could perform even better with the aid of humans? That’s the pitch Unanimous AI, a startup headquartered in San Francisco, has been giving for the better part of four years. There’s merit to it: In a study conducted with the Stanford University School of Medicine, the startup‘s system diagnosed pneumonia “significantly” more accurately — 22 percent — than a team of radiologists working alone and reduced errors by 33 percent.

2018-09-10 00:00:00 Read the full story.

 

Understanding the Math behind the XGBoost Algorithm

Ever since its introduction in 2014, XGBoost has been lauded as the holy grail of machine learning hackathons and competitions. From predicting ad click-through rates to classifying high energy physics events, XGBoost has proved its mettle in terms of performance – and speed. I always turn to XGBoost as my first algorithm of choice in any ML hackathon. The accuracy it consistently gives, and the time it saves, demonstrates how useful it is. But how does it actually work? What kind of mathematics power XGBoost? We’ll figure out the answers to these questions soon. Tianqi Chen, one of the co-creators of XGBoost, announced (in 2016) that the innovative system features and algorithmic optimizations in XGBoost have rendered it 10 times faster than most sought after machine learning solutions. A truly amazing technique!

In this article, we will first look at the power of XGBoost, and then deep dive into the inner workings of this popular and powerful technique. It’s good to be able to implement it in Python or R, but understanding the nitty-gritties of the algorithm will help you become a better data scientist.
2018-09-06 09:02:21+05:30 Read the full story.

 

Samsung opens robotics-focused AI research hub in New York City

Samsung has opened its second U.S. artificial intelligence (AI) research facility (sixth globally), as the Korean electronics giant continues to double down on its investments in transformative technologies. Samsung announced last year that it was planning a new AI research hub, and in the intervening months it actually opened centers in Canada, the U.K., and Russia, in addition to existing facilities in Seoul (South Korea) and Mountain View, California.

Its latest center, which will focus chiefly on robotics, is located in Chelsea, New York City and was officially opened at a ceremony featuring renowned AI expert Daniel D. Lee, executive vice president of Samsung Research. Lee joined the company a couple of months back and will lead the new AI center.
2018-09-10 00:00:00 Read the full story.

 

US presidential hopeful: Free money can help save the country from jobs lost to robots

Entrepreneur Andrew Yang is running for U.S. president, and he’s made the robot revolution a central pillar of his election campaign for the 2020 race.

Yang is the founder of Venture for America, a nonprofit that helps entrepreneurs create jobs, and he was previously the CEO of education firm Manhattan Prep. A Democrat, he has said the rise of automation and artificial intelligence will soon render millions of American jobs obsolete. To prevent widespread unemployment, he’s proposing monthly stipends of $1,000 for all citizens aged 18 to 64, no strings attached.
2018-09-10 00:00:00 Read the full story.

 

Why Self-Service Analytics Has Gone Backward–and What To Do About It

During the past decade, the assertion that the data warehouse is required to be the center of an enterprise data system started to break down in a variety of ways. Reasons were numerous; they included such unwanted results as increasing complexity, loss of speed and agility and increasing costs. As a result, instead of analytics becoming increasingly self-service-oriented, for the first time the world of analytics was actually going backward, away from the self-service ideal. Progress hasn’t been completely undone, but compared to where IT was a few years ago, effective and easy-to-navigate self-service has become much more difficult to achieve. As a result, analysts are much more dependent on IT tools than ever before.
2018-09-06 00:00:00 Read the full story.

 

Artificial intelligence poses greater threat than terrorism, expert warns

Jim Al-Khalili, the incoming president of the British Science Association has warned that the advent of artificial intelligence (AI) is posing more threat than terrorism in the world. The progress of AI is more rapid than previously thought and as of now, no regulations are made in this sector so far. He argued that the advent of AI in all courses of our life will lead to inequality as thousands of people will lose their jobs. He also added that the drastic rise in AI will make Britain more probe to dangerous cyber attacks.
2018-09-10 13:19:46+08:00 Read the full story.

 

From Black Box ML to Glass Box XAI (Expandanble AI)

One of the difficulties in stepping into a red-hot technology space is you’re not sure what to expect. As you’re grappling with unexpected technical curve balls what if your own stakeholders beat you to death after seeing the results which are wrong by their expectations or understanding. The current nascent implementation of Artificial Intelligence and Machine Learning models are not a smooth sailing by any stretch of the imagination either.

As much I am excited to work with the new technology and get new futuristic visions to see the sunshine of reality, I also know the labor that goes in to get it out there in a production system is no less than breaking a mountain, and what if you realize that the rocks underneath is not conducive to build a smooth road. All that effort gets dumped in a breakneck speed and with it goes the dream of bringing something new to being and get it implemented.
2018-09-08 03:14:10 Read the full story.

 

Researchers develop a method that reduces gender bias in AI datasets

Word embedding — a language modeling technique that maps words and phrases onto vectors of real numbers — is a foundational part of natural language processing. It’s how machine learning models “learn” the significance of contextual similarity and word proximity, and how they ultimately extract meaning from text. There’s only one problem: Datasets tend to exhibit gender stereotypes and other biases. And predictably, models trained on those datasets pick up and even amplify those biases

In an attempt to solve it, researchers from the University of California developed a novel training solution that “preserve[s] gender information” in word vectors while “compelling other dimensions to be free of gender influence.” They describe their model in a paper (“Learning Gender-Neutral Word Embeddings“) published this week on the preprint server Arxiv.org.

2018-09-07 00:00:00 Read the full story.

 

Trump’s battle against Silicon Valley may create an opening for China in artificial intelligence

There is little doubt that the Department of Defense needs help from Silicon Valley in order to compete with China in the race for artificial intelligence. The question is whether Silicon Valley is willing to cooperate and whether President Donald Trump’s combative nature risks damaging the vital partnership. Last week reports surfaced that Secretary of Defense Jim Mattis had warned Trump that the United States is not keeping pace with the ambitious plans of China in artificial intelligence.

Instead, Trump attacked Google, Facebook and Twitter on Twitter last week, accusing the tech giants of intentionally suppressing conservative news outlets supportive of his administration. More than that, he aggravated the rift between the government and tech industry.
2018-09-08 00:00:00 Read the full story.

 

5 Reasons Amazon May Double to $2 Trillion

Amazon Inc. (AMZN) has seen its market value jump seven-fold in just five years, becoming the second U.S. corporation to surpass the $1 trillion mark briefly on Tuesday and reaching that milestone nearly twice as fast as smartphone maker Apple Inc. (AAPL). Now, bulls see at least five forces which could double the value of the e-commerce and cloud-computing giant to reach $2 trillion, including its cloud business, surging ad sales, opening of physical stores, artificial intelligence (AI) push and health care business, according to a detailed story by CNBC.
2018-09-06 04:00:00-06:00 Read the full story.

 

BrainChip Announces the Akida™ Architecture, a Neuromorphic System-on-Chip

BrainChip Holdings Ltd., the leading neuromorphic computing company, today establishes itself as the first company to bring a production spiking neural network architecture – the Akida Neuromorphic System-on-Chip (NSoC) – to market.

This architecture announcement firmly positions BrainChip as the leader in acceleration for artificial intelligence (AI) at the edge and the enterprise. The Akida NSoC is small, low cost and low power, making it ideal for edge applications such as advanced driver assistance systems (ADAS), autonomous vehicles, drones, vision-guided robotics, surveillance and machine vision systems. Its scalability allows users to network many Akida devices together to perform complex neural network training and inferencing for many markets including agricultural technology (AgTech), cybersecurity and financial technology (FinTech).
2018-09-10 04:01:00+00:00 Read the full story.

 

A Beginners Guide To Dopamine Reinforcement Learning Framework

Reinforcement learning algorithm, soon becoming the workhorse of machine learning is known for its act of rewarding and punishing an agent. This acts as a bridge between human behaviour and artificial intelligence, enabling leading researchers to work on artistic discoveries in this domain. The recent success of Deepmind’s AlphaGo in defeating the world champion at Go and OpenAI’s Dota 2 bots thrashing the game’s veteran players with just six months of training is a notable achievement in the area of Reinforcement Learning. This versatile research platform required an environment to test the new ideas and to play with the models in the mind of researchers. This is the reason why Dopamine was built and to enhance the work of individuals and teams passionate about reinforcement learning.

Dopamine, the newest research framework released by Google, is geared at fast prototyping development of reinforcement learning algorithms. It provides that key missing piece for researchers, that is, benchmarking abilities with 60 different atari arcade games. Agents such as DQN, C51, Rainbow Agent and Implicit Quantile Network are the four-values based agents currently available.
2018-09-07 09:53:58+00:00 Read the full story.

 

When Bayes, Ockham, and Shannon come together to define machine learning

It is somewhat surprising that among all the high-flying buzzwords of machine learning, we don’t hear much about the one phrase which fuses some of the core concepts of statistical learning, information theory, and natural philosophy into a single three-word-combo. And, it is not just a obscure and pedantic phrase meant for machine learning (ML) Ph.Ds and theoreticians. It has a precise and easily accessible meaning for anyone interested to explore, and a practical pay-off for the practitioners of ML and data science. I am talking about Minimum Description Length. And you may be thinking what the heck that is…

Let’s peal the layers off and see how useful it is…
2018-09-08 22:08:19.092000+00:00 Read the full story.

 

From IBM To Mastercard; Tech Giants Are Using Predictive Analytics To Reduce Employee Attrition

Predictive analytics has been pegged as the key to addressing employee attrition. It has emerged as the missing link for the human resources department which lacks the analytical ability in bolstering their reporting. Also, the combination of right analytical approach is crucial to address the biggest pain point for HR — retaining talent. The other important issue is also about identifying the employees who have a propensity to leave, and how to retain them.

Predicting employee turnover is one of the most common use cases in HR analytics. The turnover rate can be identified in HR reporting, by assessing various parameters such as employee profile, satisfaction evaluation, performance evaluation, project planning and evaluation, absence and time sheets and communications and interaction schemas, among others. However, a certain amount of attrition is unavoidable and largely unpredictable, says a whitepaper from TCS, since companies can never gather all the data that went into each decision.
2018-09-10 06:06:36+00:00 Read the full story.

 

Scalable methods for explaining machine learning – Towards Data Science

Machine learning is often called the part of AI that works. Yet, there is a growing unease that we do not really understand why it works so well. There is also the fear of algorithms gone wild. These criticisms are not entirely fair. The stalwarts in the field who designed some of the most successful algorithms do understand why things work. However, this is a form of understanding that is as amorphous as it is deep. Machine learning algorithms are complex, and explaining complex objects is never easy. But as machine learning matures as a field and becomes ubiquitous in software, it is perhaps time to talk about more tangible forms of understanding and explanation.

In this post I will discuss why our conventional methods of understanding are not very useful for machine learning. And then I will discuss an alternate method of understanding that might be more suitable for the specific context of machine learning. Nothing that I discuss in this post is new. It should all be very familiar to the seasoned practitioners. This is merely my attempt to make explicit some of the implicit wisdom in the field.
2018-09-09 17:28:58.135000+00:00 Read the full story.

 

MIT CSAIL uses AI to teach robots to manipulate objects they’ve never seen before

Few fields have been transformed by artificial intelligence (AI) more than robotics. San Francisco-based startup OpenAI developed a model that directs mechanical hands to manipulate objects with state-of-the-art precision, and Softbank Robotics recently tapped sentiment analysis firm Affectiva to imbue its Pepper robot with emotional intelligence.

The latest advancement comes from researchers at the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who today in a paper (“Dense Object Nets: Learning Dense Visual Object
Descriptors and Application to Robotic Manipulation”) detailed a computer vision system — dubbed Dense Object Nets — that allows robots to inspect, visually understand, and manipulate object they’ve never seen before.
2018-09-09 00:00:00 Read the full story.

 

Koch Disruptive Technologies’ Jason Illian to discuss investing in AI at VentureBeat’s Blueprint event

Jason Illian, managing director of Koch Disruptive Technologies, is one of the speakers who will be appearing at VentureBeat’s upcoming Blueprint conference in York, Pennsylvania on October 9-11. At the event, speakers including Illian will discuss how both private companies and Heartland cities can prepare to capitalize on advancements in automation and AI.

Koch Disruptive Technologies is a subsidiary of the 70-year-old Koch Industries. Though Koch Industries is most well known for its oil and gas and manufacturing subsidiaries, KDT isn’t looking to invest exclusively in those industries. Since the fund started operating in November, KDT has invested in two companies: medical device company Insightec, which develops MRI-guided ultrasounds for surgery, and Mesosphere, which develops a hybrid cloud platform that helps companies automate operations for container, data engineering, and machine learning tools.
2018-09-07 00:00:00 Read the full story.

 

Deep Learning Algorithms Are Performing Calculations At The Speed Of Light

Hardware forms the core aspect of AI applications. Apart from computing power, an efficient and quick mode of relaying information between network layers is equally important. This is where optical techniques are now explored by researchers. In fact, these optical systems can be aggregated into hardware elements such as GPUs.

This article discusses a particular research study by scholars from The University of California, Los Angeles (UCLA), where the team designed neural networks for two tasks, handwritten digit recognition and as an imaging lens.
2018-09-10 09:02:05+00:00 Read the full story.

 

Gadi Singer interview — How Intel designs processors in the AI era

Intel is the world’s biggest maker of processors for computers, but it hasn’t been the fastest when it comes to capitalizing on the artificial intelligence computing explosion. Rival Nvidia and other AI processor startups have jumped into the market, and Intel has been playing catchup.

But the big company has been moving fast. It acquired AI chip design firm Nervana in 2016 for $350 million, and Intel recently announced that its Xeon CPUs generated $1 billion in revenue in 2017 for use in AI applications. Intel believes that the overall market for AI chips will reach between $8 billion and $10 billion in revenue by 2022. And the company is focused on designing AI chips from the ground up, according to Gadi Singer, vice president and general manager of AI architecture at Intel.
2018-09-09 00:00:00 Read the full story.

 

Years after patenting the concept, Amazon admits putting workers in a cage would be a bad idea

Warehouse workers confined in cages? That’s the dark vision evoked by an essay delving into the worries that come along with the development of artificial-intelligence devices such as the Amazon Echo speaker.

“Anatomy of an AI System” was published on Friday by the AI Now Institute and Share Lab — and it’s already gotten a rise from the executive in charge of Amazon’s distribution system, who says the cage concept never ended up being used.
2018-09-08 20:40:04-07:00 Read the full story.

 

Market Basket Analysis on Online Retail Data – Towards Data Science

Have you ever noticed that bread and milk are often far away from each other in a grocery store, even though they are normally purchased together? Why is that? That’s because they want you to walk all over the store and notice other items inbetween bread and milk and perhaps buy some more items. This is a perfect example of an application of Market Basket Analysis (MBA). MBA is a modeling technique based upon the theory that if you buy a certain set of items, you are more or less likely to buy another set of items. It is an essential technique used to discover association rules that can help increase the revenue of a company.

In one of my previous post (Preprocessing Large Datasets: Online Retail Data with 500k+ Instances) I explained how to wrangle a huge data set with 500000+ observations. I am going to use the same data set to explain MBA and find the underlying association rules.
2018-09-08 20:53:32.978000+00:00 Read the full story.

 

Standard Cognition beats Amazon to cashierless store in San Francisco

Startup Standard Cognition today announced plans to open a cashierless store in San Francisco in the coming days. Named Standard Market, the store will operate with limited hours and is a testing site for Standard Cognition’s artificial intelligence that uses cameras to track the movement of shoppers throughout a store.

When you arrive at Standard Market, located at 1071 Market Street, you use the Standard Checkout app to check in, then just walk out with whatever you want. Initial products for sale in the 1,900-square foot store will include a mix of food, cleaning supplies, and general household or convenience store items.
2018-09-07 00:00:00 Read the full story.

 

Weekly Selection — Sep 7, 2018 – Towards Data Science

 

  • Practical Advice for Data Science Writing
  • Recurrent Neural Networks: The Powerhouse of Language Modeling
  • Probability concepts explained: Rules of probability
  • Deep learning and Soil Science (Part 1, Part 2)
  • How to Create Animated Graphs in Python
  • Storytelling for Data Scientists
  • How machines understand our language: an introduction to Natural Language Processing

2018-09-07 16:19:09.528000+00:00 Read the full story.


Behind a Paywall

 

NHS will need 50 per cent more staff in a decade if it does not embrace technology

he NHS will need 50 per cent more staff in a decade if it does not embrace the use of new technology like artificial intelligence, one of its top bosses has warned. Prof Ian Cumming, chief executive of Health Education England, which is in charge of NHS staffing, said persuading the public to overhaul dangerously unhealthy lifestyles was also key to lifting increasing demand on the service. He issued the stark warning as the Government prepares to publish a 10 year plan for the health service.
2018-09-08 00:00:00 Read the full story.

 

 


This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors.

This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.