3 Top Artificial Intelligence Stocks to Buy in 2017

Computer-assisted data analysis is not exactly a new thing, but these tools are becoming incredibly powerful. Here are three ideas for investors who want to jump aboard this careening bandwagon.

Artificial intelligence (AI) is a hot topic nowadays. Computer chips and clever programming can accelerate and improve an ever-growing numbers of tasks normally reserved for the intuitive thinking skills of human beings.

The number of ways to invest in this unstoppable trend is also large and growing. Any technology specialist worth its salt is at least investigating the artificial intelligence space right now, either as a product for their clients or as back-end tools for their own use. Therefore, what follows is not an attempt to cover the entire universe of artificial intelligence stocks but just a handful of the most investable names in that market today. On that note, I'll take a look at Samsung (NASDAQOTH:SSNLF), salesforce.com (NYSE:CRM), and IBM(NYSE:IBM) through the lens of artificial intelligence.

IMAGE SOURCE: SALESFORCE.

Everything is relative in sales and marketing

You know Salesforce as a maker of cloud-based tools for management of customer relationships. Most of the time, this business amounts to automating or at least streamlining repetitive tasks in the process of finding customers and closing sales. The company's innovative approach to a set of traditional business problems has rewarded investors with a market-crushing 660% return over the last decade.

Now, the cloud computing veteran is taking the next logical step by building an artificial intelligence platform to analyze customer data. Salesforce Einstein was built out of several small acquisitions, bringing together their specialized tools under one unified umbrella. So Einstein takes the sales and marketing process, bakes in elements of machine learning, natural language processing, data analytics, relationship intelligence, and much more as a user-friendly all-in-one data management helper.

The platform was introduced last fall and rolled out to customers earlier this month. These are early days for Salesforce's artificial intelligence ambitions, but Einstein is designed to learn from experience and get better over time. The more data Einstein gets to process, the more sophisticated its results will become.

And so will Salesforce's overall efforts in this emerging market. If you're not buying in right now, make sure to at least keep an eye on Salesforce Einstein as the platform matures.

IBM WATSON FACING OFF AGAINST TWO ELITE HUMAN "JEOPARDY!" PLAYERS. IMAGE SOURCE: IBM.

It's elementary, my dear IBM Watson

Big Blue is a veteran in the artificial intelligence market.

Way back in 1996, the company built a chess-playing supercomputer capable of challenging world champion Garry Kasparov. Kasparov won that first match but Deep Blue collected two draws and one outright win along the way.

The next year, Kasparov lost the rematch against an upgraded Deep Blue. Thus began a new era in the contest between man and machine, with IBM carrying the robotic banner.

Kasparov moved on to promote computer-assisted chess, conceding that he -- the best human chess player -- no longer could challenge the best computer systems. And computers have only improved since then, giving humans no chance to keep up. "Today you can buy a chess engine for your laptop that will beat Deep Blue quite easily," Kasparov said in a podcast interview with ChessBase last year.

Encouraged by this early success, IBM dug deeper into artificial intelligence with qualities of human-like intuition. In 2011, the IBM Watson system beat Jeopardy! champions Ken Jennings and Brad Rutter at their own game. That system was powered by analysis of 200 million documents, including the then-current full text of Wikipedia.

Now, the Watson system has become the linchpin of IBM's overall strategy. The company recently reorganized to create a reportable business segment focused on Watson's cognitive computing. That segment already accounts for more than 40% of Big Blue's annual sales and more than half of its operating profits.

Watson is promoted as a catch-all platform for data analysis and human-to-machine interaction. The system "can understand all forms of data, interact naturally with people, and learn and reason, at scale," according to IBM's marketing materials.

If a few hundred million documents were enough to beat Ken Jennings at Jeopardy! six years ago, the system has evolved much further -- with hand-curated access to the entire internet and tons of private training data along the way. If I had to pick an artificial intelligence tool to assist in a life-or-death situation, Watson would be it.

And IBM is already making big money from that market-defining advantage. The best is yet to come.

Bixby, help me sell this Galaxy S8 phone

Finally, the most obviously consumer-facing pick on this list comes from South Korea.

No, you probably don't think of Samsung as an authority on artificial intelligence -- yet. That's about to change, if the Koreans have their way.

The Galaxy S8 smartphone flagship is hot off the presses, and comes with Samsung's first try at an AI-powered digital assistant. The Bixby tool was built around technology from Viv, a $214 million acquisition in October of 2016 that brought in talent originally associated with Apple's Siri assistant.

Like Watson and Einstein, Bixby knows how to communicate with humans using natural language instead of hand-picked key words. Samsung hopes to get its users accustomed to controlling their phones largely by voice, making on-screen keyboards irrelevant in the long run. Bixby has the artificial intelligence savvy that's needed to start moving in that direction.

Where existing digital assistants in the Siri mold tend to create a voice-driven bridge between the user and various online services, Bixby prefers to interface with the handset's internal operation. "Anything you can do with touch, you can do with voice," is the Samsung mantra here. The camera is also tightly integrated into Bixby, reaching for that elusive experience of augmented reality through your Samsung phone.

Bixby is a major talking point for the Galaxy S8, and Samsung hopes to make it a major selling point as well. The system may need to earn its wings by trial and error in real-world usage, but Samsung has the resources and motivation to make it work in the long run.

Samsung's Galaxy S phones have come a long way since a fairly low-key start in 2009. Expect Bixby to follow a similar trajectory, until it starts showing up in Samsung's TV sets and refrigerators. This is till the very early going of the whole artificial intelligence race.

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Le talon d’Achille de l’intelligence artificielle

Les algorithmes d’apprentissage sont à l’origine des progrès extraordinaires de l’intelligence artificielle. Mais certains d’entre eux ont un point faible : impossible d’expliquer leur fonctionnement.

Eric FEFERBERG /AFP

lls sont à l'origine des dernières avancées les plus spectaculaires de l'intelligence artificielle, qu'il s'agisse d'analyse d'images, de reconnaissance vocale ou de traduction. On les appelle les réseaux de neurones profonds, mais ce sont en fait des algorithmes qui permettent aux machines d'apprendre par elles-mêmes - le fameux « machine learning ». Le problème, c'est que personne, pas même leurs concepteurs, n'est en mesure d'analyser par quel raisonnement ils arrivent à de si bons résultats. Les spécialistes de l'IA appellent cela « l'effet boîte noire ». Rien à voir avec les boîtes noires de l'aéronautique ou de l'automobile. C'est même tout le contraire : au lieu de donner accès facilement aux données qu'ils contiennent, les réseaux de neurones sont fondamentalement inaccessibles. On connaît les données qui y entrent, on connaît celles qui en sortent, mais entre les deux... mystère.

Cette opacité est liée à la façon dont fonctionnent ces réseaux de neurones. Avec eux, les données (chiffres, image, voix...) sont analysées par des couches successives de calcul, qui attribuent à chaque fois un coefficient, et transmettent le résultat à la couche suivante. « Pour entraîner un réseau de neurones, on va lui donner une multitude d'exemples, et en fonction de la différence entre le résultat obtenu et le résultat attendu, le système va mettre à jour ses coefficients », explique Gérard Berry, informaticien et professeur au Collège de France . Résultat : « Dans un programme informatique classique, si on connaît le code, on est capable de savoir ce qui se passe et de prévoir le résultat . Avec les réseaux profonds, on ne sait pas ce qui se passe : quand un algorithme donne une réponse, c'est comme ça. »

Opacité

Pendant plusieurs décennies, la question des « boîtes noires » n'était pas vue comme primordiale, car le « machine learning » était avant tout un sujet de laboratoire. Ce n'est plus le cas aujourd'hui : la croissance conjuguée des bases de données (indispensables pour « entraîner » les algorithmes) et des puissances de calcul (indispensables pour créer des réseaux de neurones profonds) a donné des résultats spectaculaires, poussant les géants de l'informatique, d'internet et du Big Data à recruter les meilleurs spécialistes du secteur, et à multiplier les applications . Quand Siri d'Apple reconnaît votre voix , quand Google Photos classe vos clichés de vacances par thèmes, quand Criteo recommande des publicités qui pourraient vous intéresser, c'est désormais, au moins en partie, grâce à l'apprentissage profond.

COMMENT « VOIENT » LES ALGORITHMES

Les machines ne pensent pas comme nous, et celles spécialisées dans l'analyse d'images ont une vision du monde bien différente de la nôtre. Le site Deepdream Generator en fournit une illustration frappante. Il exploite un réseau de neurones conçu par Google pour la reconnaissance d'images, qui a été publié en « open source » en 2015.

A l'origine, Deepdream a été développé pour trouver des formes récurrentes (« patterns ») dans les images. Il transforme aussi les images pour rendre ces formes visibles, montrant par l'exemple ce que les réseaux de neurones « voient » dans une photo. Le résultat produit est souvent psychédélique : dans l'image ci-dessus, une photo du discours d'Emmanuel Macron devant la pyramide du Louvre a été traitée par une version de Deepdream entraînée à reconnaître les animaux.

Lors d'une rencontre organisée fin avril chez Google France, Olivier Bousquet, responsable du machine learning au laboratoire de recherche zurichois du groupe, décrivait ainsi le fonctionnement de Google Traduction : « C'est un gigantesque réseau de neurones qui a appris tout seul à passer d'une langue à l'autre. Dans certains cas, il réussit à être meilleur que les traducteurs humains. L'autre chose étonnante est que l'on ne lui a appris que quelques paires de langues, et qu'il en a déduit les autres. Il a créé une sorte d'espéranto tout seul. Mais on n'arrive pas encore bien à le décrypter. »

Pour beaucoup de chercheurs, comme pour les pouvoirs publics, comprendre ces « boîtes noires », ou du moins rendre leur fonctionnement plus compréhensible, va devenir un enjeu majeur. En particulier si on veut les autoriser pour des applications jugées critiques, que ce soit pour la sécurité (pilotage des voitures autonomes), la médecine (analyse automatique des radios, prédiction de survenue d'un cancer), la défense (drones autonomes), etc. « Quand on cherche à savoir si une image contient un chien ou chat, l'importance de l'explicabilité est assez faible. En revanche, dans le milieu médical, les médecins vont toujours favoriser une technique qu'ils peuvent expliquer », résume Francis Bach, chercheur Inria à l'Ecole Normale Supérieure et spécialiste du machine learning.

« Le défi de l'explicabilité »

Dans un rapport publié en mars dernier, l'Opecst (Office parlementaire d'évaluation des choix scientifiques et technologiques) soulignait que « le phénomène des boîtes noires  appelle un effort de recherche fondamentale pour accroître leur transparence (...) Le défi à relever est donc celui de l'explicabilité. » Il est également au coeur des questions de transparence des algorithmes, demandée avec insistance par la Commission européenne au cours des derniers mois. Mais les régulateurs du Vieux Continent ne sont pas les seuls à penser que le sujet exige un effort de recherche. Au mois d'août dernier, la Darpa (« Defense Advanced Research Projects Agency »), une agence du Pentagone à laquelle on doit les recherches qui ont donné naissance à Internet, au GPS ou aux véhicules autonomes, a lancé un nouveau programme, intitulé « Explainable Artificial Intelligence » (XAI). Il vise à « créer des technologies de machine learning qui produisent des modèles plus explicables, tout en maintenant un haut niveau de performances, et à permettre aux humains de comprendre, d'avoir une confiance réelle et de pouvoir gérer efficacement la génération émergente d'outils d'IA ».

Le sujet est aussi sur la table du centre Google Research de Zurich , expliquait fin avril son directeur, Emmanuel Mogenet : « Il y a beaucoup d'efforts de recherche pour extraire le raisonnement de la machine, pour faire du « reverse engineering » (NDLR : analyse d'un produit pour en comprendre le fonctionnement ). C'est quelque chose d'extrêmement important, car il y a un certain nombre de domaines où cela peut poser problème. »

Reste que, pour l'heure, personne ne sait comment rendre les réseaux de neurones moins opaques. Il est certes possible de contrôler leurs résultats, par exemple en mettant plusieurs systèmes en concurrence, ou en utilisant des techniques pour savoir quelles variables d'entrée ont pris une importance prépondérante dans le résultat final. Mais cela se fait au détriment de la rapidité du traitement, voire de sa pertinence. « Est-ce que l'on est prêt à réduire la performance de prédiction pour obtenir une explicabilité ? », interroge Francis Bach.

A défaut de pouvoir expliquer le « raisonnement » des algorithmes de deep learning, une autre approche consiste à rendre visible la façon dont ils fonctionnent. Le fabricant de microprocesseurs NVidia a ainsi développé un système pour voitures autonomes, basé sur le deep learning, qui a appris la conduite sans règles, en se basant uniquement sur la façon de conduire de nombreux humains. Originalité, ce système montre clairement quels éléments sont les plus importants dans sa prise de décisions - les piétons, les voitures ou les éléments signalisation apparaissent en surbrillance sur les images que produit et exploite le système. Pour Laurence Devillers, chercheuse au laboratoire d'informatique pour la mécanique et les sciences de l'ingénieur (Limsi) du CNRS et auteur de l'essai « Des robots et des hommes » (Plon, 2017) , « s'il est important de rendre ces systèmes plus robustes, il faut aussi expliquer comment ils fonctionnent, et garder à l'esprit qu'ils apprennent sans comprendre, donc qu'ils décident sans comprendre. »

Reste que la question des boîtes noires divise encore les chercheurs. Pour Yann Le Cun , l'un des pionniers des réseaux de neurones, désormais responsable du laboratoire FAIR (Facebook Artificial Intelligence Research), « ce n'est pas un problème majeur. C'est très satisfaisant d'avoir une explication, et cela rassure l'humain si un système d'intelligence artificielle produit une explication. Mais, à la fin, ce que l'on veut, c'est avant tout une bonne fiabilité », indiquait-il début mai, à l'occasion d'une rencontre sur l'intelligence artificielle organisée par Facebook Paris. Très engagé dans les questions d'éthique de l'IA, Yann Le Cun a coutume de dire que le cerveau humain est lui aussi une boîte noire, et que nous avons appris à nous en accommoder. « Ce n'est pas complètement faux, reconnaît Gérard Berry. Mais nous avons l'habitude de traiter avec les humains, et nous connaissons à l'avance le type d'erreurs qu'ils font. Dans le cas de l'IA, la discipline est encore un peu jeune pour tirer ce genre de conclusions. »

Benoît Georges

Research Associate In Artificial Intelligence For Data Analytics

3 Research Associates In Artificial Intelligence For Data Analytics
British Library, London 
£35,000 per annum (negotiable dependent on skills & experience)
Full time
Permanent
15th June 2017

Opportunity for 3 Research Associates to work on advanced methods from artificial intelligence and machine learning to help automate the data analytics process. These posts will be based at the Alan Turing Institute hub at the British Library in London.

The process of transforming a raw dataset into useful knowledge is called data analytics. It comprises many different stages and phases. While some elements of the data analytics process have benefited from considerable development through software or tools, there has been little methodological research into so-called data ‘wrangling’, even though this is often laborious and time-consuming, and accounts for up to 80% of a typical data science project. Data wrangling includes understanding what data is available, integrating data from multiple sources, identifying missing, messy or anomalous data, and extracting features in order to prepare data for computer modelling.

In the Artificial Intelligence for Data Analytics (AIDA) research project, we will draw on new advances in artificial intelligence and machine learning to address data wrangling issues; we aim to develop systems that help to automate each stage of the data analytics process.  It is anticipated that the resulting technology will benefit researchers, industry and government, dramatically improve the productivity of working data scientists, and revolutionise the speed and efficiency with which data can be transformed into useful knowledge.

The AIDA team will consist of the investigators (Chris Willams, James Geddes, Zoubin Ghahramani, Ian Horrocks, Charles Sutton), the three research assistants, software engineers, data scientists, and aligned PhD students.

We invite applications from talented and qualified researchers to become part of the AIDA research project.

THE ROLES

The AIDA project will build an intelligent data analytics system that guides an analyst through a semi-automated process of acquiring, preparing, integrating, transforming, cleaning, and understanding data for analysis. AIDA will aid data preparation by combining technologies from logical artificial intelligence and statistical machine learning. Logic-based AI contributes by providing a powerful set of tools for integrating and representing metadata about data whose complexity and heterogeneity makes it impossible to represent as a set of relational tables. Statistical machine learning provides a powerful set of techniques for inferring what is “typical” for a data source, which can underlie new techniques for identifying low-quality data, for evaluating the effect of data transformations, and for summarizing data by reporting on typical behaviour.

The three Research Associates (RAs) will take on different aspects of the work programme as described below. They will work as part of the AIDA team, and collaborate with the software engineers in order to create a unified system. These roles are initially on a three year fixed term contract.

RA1 will work on Data Acquisition and Transformation. The goal will be to develop a data acquisition component (DAC) that uses semantic technology to support analytics tools in the acquisition and transformation of relevant data from large, distributed, dynamic, and heterogeneous data sources. The DAC will provide comprehensive data restructuring functionality, which will require the extension of semantic technologies with features such as aggregation. We will also investigate the use data analysis techniques to reveal hidden/lost data structure, and the use of axiomatic data structure to support data understanding, cleaning and analysis.

RA2 will work on Data Understanding. The main tasks here will be around helping the data analyst to build their intuition about what is in the data, looking both for common patterns and anomalies.  This will include work using techniques from statistical machine learning (or related areas) to infer compact summaries of datasets, automatically inferring the types of variables from data, and producing simple reports describing statistically reliable patterns in data.

RA3 will work on Data Quality and Cleaning. Here we identify important tasks on handling missing data, entity disambiguation, and identifying anomalies, and passing on possible uncertainty in these components to later stages of the analysis. We will explore ways of developing new data quality methods, exploiting particularly the methods of statistical machine learning, possibly in combination with semantic technologies.

DUTIES & RESPONSIBILITIES

The RAs will work with the AIDA team (including the investigators, software engineers, data scientists and PhD students) to advance the aims of the AIDA project.

The main responsibilities of the posts are:

• To conduct research on topics in artificial intelligence for data analytics;
• To collaborate in the preparation of reports, publications and presentations;
• To collaborate and/or present findings at academic and practitioner conferences and meetings.

PERSON SPECIFICATION

Essential:

• A PhD or equivalent qualification in a relevant discipline (at the point of taking up the position);
• A proven publication record in a relevant discipline (e.g. in peer-reviewed academic journals and other high-quality publication venues);
• Appropriate software skills for the implementation of research software;
• Excellent communication and interpersonal skills, for interaction with the AIDA research team and project partners, and the ability to present complex technical information.

Desirable:

• Experience in working in semantic technologies and/or statistical machine learning, or related fields.

THE ALAN TURING INSTITUTE

The Alan Turing Institute (the Turing) is the national centre for data science, established in 2015 with the mission to make great leaps in transformational data science research that will have positive real-world impacts.

The Institute has cross-disciplinarity at its core; we bring researchers in mathematics and theoretical computer science, statistics and machine learning, algorithm for data analytics and distributed computing, computational social science and data ethics, and industry partners, to work together in an open and collaborative environment with a shared goal to generate world-class research in data science.

Our researchers are motivated by driving impact, both through theoretical development and application to real-world problems. In our first year we have identified six priority sectors to focus our translational research: Data-Centric Engineering; Defence and Security; Smart Cities; Culture and Media; Financial Services; and Health and Wellbeing.

We have attracted strategic partnerships with a broad range of users of data science including the Lloyd’s Register Foundation, Intel, GCHQ and HSBC. We are looking to develop partnerships with government departments and have recently announced a collaboration with the Office of National Statistics.

We invite you to join us as we grow our research community, supporting our goal to develop the next generation of data science leaders, shape the public conversation, and push the boundaries of this new science for the public good.

APPLICATION PROCEDURE

This is an exciting opportunity to join a diverse, growing organisation with a strong team of individuals at the helm. There is a generous benefits package including Health Care, enrolment into a generous pension scheme, Cycle2work and childcare vouchers. The Institute also offers 30 days’ holiday per year exclusive of bank holidays.

If you are interested in this opportunity, please send your CV, with contact details for your referees and a covering letter tojobs@turing.ac.uk. If you have questions or would like to discuss the role further with a member of the Institute’s HR Team, please contact them on 0203 862 3322 or emailHR@turing.ac.uk.

CLOSING DATE FOR APPLICATIONS: 16th JUNE 2017

INTERVIEWS WILL TAKE PLACE W/C 10th JULY 2017

Note if you do not hear back from us by the 5th July 2017, please assume your application has not been successful.

The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly.  In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital status, pregnancy, religion or belief or sexual orientation. Reasonable adjustments to the interview process can also be made for any candidates with a disability.

The appointment of overseas applicants that require Certificates of Sponsorship to work in the United Kingdom will be subject to the Home Office granting the Alan Turing Institute a Tier 2 visa sponsor licence, the application for which is currently in progress.

Please note all offers of employment are subject to continuous eligibility to work in the UK and satisfactory pre-employment security screening which includes a DBS Check.

Full details on the pre-employment screening process can be requested from HR@turing.ac.uk.

Jeff Bezos says that we're living in the golden age of AI

"There’s no institution in the world that cannot be improved with machine learning.”

Image: REUTERS/Kevork Djansezian

AI BOON AND BOOM

For Amzon founder and Blue Origin CEO Jeff Bezos, artificial intelligence (AI) isn’t something to be feared. Instead, it’s to be celebrated. Speaking at the Internet Association’s annual gala in Washington D.C., the renowned tech entrepreneur told the audience that AI is now enjoying a renaissance, as it helps to solve problems previously seen only as science fiction.

“It is a renaissance, it is a golden age,” Bezos said. “We are now solving problems with machine learning and artificial intelligence that were … in the realm of science fiction for the last several decades. And natural language understanding, machine vision problems, it really is an amazing renaissance.”

Instead of looking at AI as the harbinger of humanity’s downfall, the Amazon founder sees it as an “enabler” that “will empower and improve every business, every government organization, every philanthropy — basically there’s no institution in the world that cannot be improved with machine learning,” he explained.

AI, AMAZON, AND BEZOS

Of course, one can argue that AI can be referred to in two ways: as its current machine learning-based state, and asa future form of superintelligence. Bezos was obviouslyreferring to the former when he explained how Amazon’s been using AI through Alexa and more.

“[A] lot of the value that we’re getting from machine learning is actually happening beneath the surface. It is things like improved search results,” Bezos said. “Improved product recommendations for customers. Improved forecasting for inventory management. Literally hundreds of other things beneath the surface.”

Indeed, apart from improving services like Alexa, AI’s ability to learn has given it near human — or even better than human — skills. AI can now drive cars and see the way human drivers do. AI can do serious work as doctors,lawyers, or as burger flippers. It can even play competitive games against human players. AI can now also become artists by composing music or by painting.

Indeed, we are in a golden age of AI.

Artificial Intelligence ETF List

Artificial Intelligence is an area of computer science that focuses the creation of intelligent machines that work and react like humans. Artificial Intelligence ETFs potentially stand to benefit from increased adoption and utilization of artificial intelligence, including those involved with industrial and non-industrial robotics, automation, 3D printing, natural language processing, social media, and autonomous vehicles.

Artificial Intelligence ETFs are funds that meet at least one of the following three criteria:

• They are funds that specifically invest in companies involved in the development of new products or services, technological improvements in scientific research related to artificial intelligence, or

• They are funds that have at least 25% of portfolio exposure to companies that spend large amounts on artificial intelligence research and development (R&D) expenses. Examples of such companies are Amazon, Tesla Motors, Apple and Alphabet, or

• They are funds that use artificial intelligence methodologies to select individual securities for inclusion into the fund.

Click on the tabs below to see more information on Artificial Intelligence ETFs, including historical performance, dividends, holdings, expense ratios, technical indicators, analysts reports and more. Click on an ETF ticker or name to go to its detail page, for in-depth news, financial data and graphs. By default the list is ordered by descending total market capitalization.

ETF Overview

This is a list of all Artificial Intelligence ETFs traded in the USA which are currently tagged by ETF Database. Please note that the list may not contain newly issued ETFs. If you’re looking for a more simplified way to browse and compare ETFs, you may want to visit our ETFdb Categories, which categorize every ETF in a single “best fit” category.

* Assets in thousands of U.S. Dollars.

Symbol	Name	Price	Change	Assets*	Avg Vol	YTD
QQQ	PowerShares QQQ ETF	$138.60	0.22%	$50,460,453.55	19,289,980.0	17.22%
XLK	Technology Select Sector SPDR Fund	$55.56	0.25%	$17,781,438.24	7,821,705.0	15.32%
VGT	Vanguard Information Technology ETF	$142.44	0.27%	$13,064,044.72	423,647.0	17.55%
FDN	First Trust Dow Jones Internet Index Fund	$94.01	0.09%	$4,375,543.78	336,364.0	17.76%
IYW	iShares U.S. Technology ETF	$142.12	0.31%	$3,531,228.74	195,637.0	18.50%
IGM	iShares North American Tech ETF	$145.17	0.20%	$1,198,546.47	25,870.0	17.44%
IXN	iShares Global Tech ETF	$131.41	0.32%	$1,148,675.50	36,311.0	19.59%
XT	iShares Exponential Technologies ETF	$31.17	0.58%	$1,032,428.90	132,623.0	16.39%
FTEC	Fidelity MSCI Information Technology Index ETF	$43.19	0.23%	$905,154.39	226,417.0	17.29%
ROBO	Robo-Stox Global Robotics and Automation Index ETF	$34.02	0.21%	$530,164.60	231,270.0	18.74%
PNQI	PowerShares NASDAQ Internet Portfolio ETF	$102.92	0.51%	$385,623.00	24,978.0	24.54%
BOTZ	Global X Robotics & Artificial Intelligence Thematic ETF	$18.09	0.39%	$87,729.32	98,669.0	20.60%
QYLD	Recon Capital NASDAQ 100 Covered Call ETF	$23.62	0.00%	$80,057.42	25,159.0	7.96%
ARKQ	Industrial Innovation ETF	$28.42	0.35%	$43,965.35	17,534.0	29.42%
BUZ	Sprott BUZZ Social Media Insights ETF	$27.55	-0.04%	$8,283.44	2,455.0	5.19%
TCHF	iShares Edge MSCI Multifactor Technology ETF	$35.16	0.11%	$3,514.24	762.0	16.81%