AI News, BOOK REVIEW: Investments by Tech Giants In Artificial Intelligence is Set to Grow ... artificial intelligence

3 Top Stocks in Artificial Intelligence

From the recommendation algorithms you see on e-commerce or streaming video sites, to fraud detection at major banks, to training doctors and performing breakthrough medical research, AI is driving some of the most exciting innovation in the world today.

However, the following three companies are undisputed leaders in their respective categories and would make great core holdings for any AI-themed portfolio.  AI requires the collection of massive amounts of data, which is then fed into a machine-learning engine that 'trains' an algorithm.

All of this increasingly takes place in the public cloud, which has lots of advantages over company-owned data centers, including the cost of storage, the flexibility to spin up and down capacity as needed, and access to leading-edge tools from the deep-pocketed cloud giants.

Another clear leader in AI is NVIDIA (NASDAQ:NVDA), the industry leader in graphics processing units (GPUs), which have traditionally been used in processing high-end graphics for video games and other visual applications.

On the third-quarter conference call with analysts, Huang also discussed some exciting growth drivers for NVIDIA, which has now adapted its new T4 data center GPUs not only for machine learning but now also for inference, specifically in speech training algorithms.

With the need to process massive data sets and execute quick, instantaneous inference, NVIDIA GPUs will need to be deployed in large numbers in the future, and NVIDIA's formidable moat makes it an undisputed leader in the space.

Micron is one of three leading producers of dynamic random access memory (DRAM), which feeds data to a processor, and one of six producers of NAND flash, one of the two main storage technologies.

Micron just unveiled its first 3D Xpoint solid-state drive in October, and while this new memory won't move the needle versus its other divisions for some time, it could differentiate Micron from others, as the only company to have all three technologies.  AI, along with other new tech applications like 5G communications, will cause a boom in memory demand in the years ahead.

The AI Advantage of the Tech Giants: Amazon, Facebook, and Google

If one tunes into social media, it’s easy to be convinced that every AI startup is making a lot of money, making a big difference in the companies that they’re working with, and driving real revenue.

There’s nothing wrong with that, but even companies that have raised $50 million are still very much iterating on what their product really is,  how they have to sell it into the enterprise, who their exact customer is, and what their core value proposition is.

It seems that there’s actually a lot more profit in the event companies than there are in many of the vendor companies even if they’ve raised $20 million because, really, the vendor companies have to solve hard problems about how to integrate AI into existing businesses.

If we understand why the big tech firms have a big edge when it comes to getting a real return on investment from artificial intelligence, this will open up new insight for business leaders who want to be able to apply these technologies on their own.

In this article, we’re going to examine these critical factors that make the Facebooks and the Netflixes of the world really able to leverage AI on a completely different level than average firms, even those with revenues that rival Facebook’s.

But also, when a company is known for being high-tech, fast-growing, even cool, the people who graduate from MIT, Georgia Tech, and the University of Illinois with a computer science PhD or machine learning concentration are very quickly going to find themselves in Silicon Valley or Seattle if they aim for Amazon.

company can pay a data scientist fresh out of school a quarter million dollars a year, but if they feel as though they are being slowed down, they won’t stay at the company.

If a machine learning graduate’s friends, who went to work for Google, are doing more exciting, meaningful, things as soon as they land their job, and no one is giving that machine learning graduate any data to work with, they’ll find another job.

They think they can hire data science talent to say that their company is doing AI, but that data scientist is likely to find work elsewhere unless they’re given something exciting to work on.

The subject matter experts at even extremely large enterprises don’t know how to articulate problems to data scientists, and so they can’t really provide them with meaningful work.

IT personnel that went to school in the 90s and early 2000s are likely to never have worked with machine learning models, let alone learn to integrate them into existing systems in business.

Established firms will hire many data scientists and spend a lot of money on them, but because their subject matter experts and IT personnel don’t know how to articulate problems to data scientists let alone integrate machine learning solutions, they struggle to keep their data scientists busy and interested.

There are probably some systems at Google, Facebook, and Amazon that were built without big data and machine learning in mind in the early days that make it difficult to train AI models on the data they store.

When someone is on, whether on their phone or laptop, every click of their mouse, every scroll on their screen, every item they add or remove to their cart, and everything they’ve ever purchased on Amazon for the last eight years is tracked by Amazon.

Netflix’s recommendation engines are much more complex than most people assume they are, and are indeed, again, predicated on artificial intelligence on a digital platform in a virtual world where everything is potentially trackable.

Even if they do instrument all these oil wells with sensors, not every oil well is built the same, and these sensors are going to send the wrong signals when it’s too hot or it’s too cold outside.

If a company can get access to all of the valuable data in a given space and can provide a better product and user experience than everyone else because of it, they will be able to continue to get access to more of that data than anyone else They will continue to pull away with an almost monopolistic power.

This means they can continually inform and update their algorithm to find the most relevant answers, to run more tests, to run more examples of new permutations of their kind of search algorithm in order to find the destination pages that are most likely to serve their users.

They now understand that there will be some digital platform that likely dominates a certain business use case, by virtue of it providing a better product than its alternatives, will continue to garner more and more users, more and more business, more and more data.

The advantage of tech giants might be summed up in the following statement: Tech giants are digitally-native companies that are built on accessible data, that have massive funds and massive technology aims which help to attract the best tech talent.

But business leaders who understand and address these challenges as they move forward will be at a much greater advantage compared to companies that don’t when it comes to leveraging AI and seeing rewards in terms of revenue, efficiencies, and market share.

Meiya Pico: from mobile data extraction to the Belt and Road’s ‘safety’ and security corridor

In November 2019, internal Communist Party documents—obtained by the International Consortium of Investigative Journalists (ICIJ)—provided documentary evidence of how authorities in Xinjiang are using data and artificial intelligence to pioneer a new form of social control.94 The documents showed how authorities are using a data management system called the Integrated Joint Operation Platform (IJOP)—previously reported on by Human Rights Watch—to predictively identify those suspected of harbouring extremist views and criminal intent.95 Among the documents, a bulletin published on 25 June 2017, reveals how the IJOP system detected about 24,412 “suspicious” people in southern Xinjiang during one particular week.

surveillance industry boom was born out of the central government’s 2015 policy to prioritise ‘stability’ in Xinjiang107 and the national implementation of the Sharp Eyes surveillance project from 2015 to 2020.108 As of late 2017, 1,013 local security companies were working in Xinjiang;109 that figure excludes some of the largest companies operating in the region, such as Dahua and Hikvision, which had already won multimillion-dollar bids to build systems to surveil streets and mosques.110 Also in 2017, even with the central government halting some of the popular ‘PPP’ projects (public– private partnerships that channel private money into public infrastructure projects) that were debt hazards111 and tech companies becoming more cautious about investing in those projects, Xinjiang was an exception for about a year.

‘Fanghuiju’ is a government initiative that sends cadres from government agencies, state-owned enterprises and public institutions to regularly visit and surveil people.124 The China Unicom fanghuiju units were reportedly tasked with changing the villages, including villagers’ thoughts that are religious or go against CCP doctrines.125 Adding some of China’s more well-known technology and surveillance companies to the US Entity List was largely symbolic—after Huawei, Dahua and Hikvision were blacklisted in the US, Uniview’s president told reporters that, at a time when ‘leading Chinese technology companies are facing tough scrutiny overseas’, companies such as Uniview had the opportunity to grow and pursue their overseas strategies.126 Unfortunately, it’s extremely difficult for international authorities to sanction the circa 1,000 homegrown local Xinjiang security companies.

The announcement of one Huawei public security project in Xinjiang—made in 2018 through a government website in Urumqi127—quoted a Huawei director as saying, ‘Together with the Public Security Bureau, Huawei will unlock a new era of smart policing and help build a safer, smarter society.’128 In fact, some of Huawei’s promoted ‘success cases’ are Public Security Bureau projects in Xinjiang, such as the Modular Data Center for the Public Security Bureau of Aksu Prefecture in Xinjiang.129 Huawei also provides police in Xinjiang with technical support to help ‘meet the digitization requirements of the public security industry’.130 In May 2019, Huawei signed a strategic agreement with the state-owned media group Xinjiang Broadcasting and Television Network Co.

Huawei was reportedly able to process and analyse footage quickly and conduct precise searches in the footage databases (for example, of the colour of cars or people and the direction of their movements) to help solve criminal cases.135 Since mass detentions began in Xinjiang over two years ago, state-affiliated technology companies such as those covered in this report have greatly expanded their remit and become a central part of the surveillance state in Xinjiang.

The Best Artificial Intelligence Stocks of 2019 -- and The Top AI Stock for 2020

Artificial intelligence (AI) -- the capability of a machine to mimic human thinking and behavior -- is one of the biggest growth trends today. Spending on AI systems will increase by more than two and a half times between 2019 and 2023, from $37.5 billion to $97.9 billion, for a compound annual growth rate of 28.4%, according to estimates by research firm IDC.

There are two broad ways you can get exposure to the AI space: With this background in mind, let's look at which AI stocks are performing the best so far this year (through Nov. 25) and which one is my choice for best AI stock for 2020.  The following chart isn't meant to be all-inclusive, as that would be impossible, and the chart has limits on the number of metrics.

Graphics processing unit (GPU) specialist NVIDIA (NASDAQ:NVDA), e-commerce and cloud computing service titan Amazon, computer software and cloud computer service giant Microsoft, Google parent and cloud computing service provider Alphabet, old technology guard and multifaceted AI player IBM, and Micron Technology, which makes computer memory chips and related storage products, would best be put in the first category above.

iPhone maker Apple (NASDAQ:AAPL), social media leader Facebook (NASDAQ:FB), video-streaming king Netflix, and Stitch Fix, an online personal styling service provider, would best be categorized in the second group since they're either primarily or solely using AI to improve their products and services.  Now let's look at some basic stats for the three best performers of this group.  Data sources: YCharts (returns) and Yahoo!

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