Conoceanastasia

2019

Artificial IntelligenceINTRODUCTION AND CONCEPTS

Content

Introduction...........................................................................................................................................01

Artificial Intelligence: Presente and Future............................................................................03

Artificial Inteligence: Levels of complexity.............................................................................04

Big Data and and Artificial Intelligence ..................................................................................07

Data Scientist vs IA Engineers .....................................................................................................09

Artificial Inteligence: Applications..............................................................................................11

Artificial Inteligence: Opportunities and Threats ................................................................12

Conclusions ...........................................................................................................................................15

References .............................................................................................................................................16

01 Introduction / What is Artificial Intelligence?

The eruption of Artificial Intelligence (AI), both in people’s daily lives and in the world of business, where business has driven the massive use of this concept remains difficult to describe. While there is no general consensus about it, a broad definition refers to "AI” [as] a branch of computer science in which machines perform tasks similar to those of the human mind such as learning or reasoning" (1).According to Gartner AI is "a discipline of computer engineering" that, in its current state, consists of software tools focused on solving specific problems. In this scenario some current AI solutions can give the impression of being intelligent but it would be a mistake to consider them to be similar or equivalent to human intelligence (2). Nevertheless, the incorporation of applied AI is one of the most promising areas of the new digital era.

In September 2018, the International Data Corporation segmented the 5 most relevant cases of AI use within the business framework. Included in this group, representing 41% of the total expenditure projected for the year 2022, are: automated agents for customer service, automated detection and threat prevention, automation and recommendation of sales processes, preventive automated maintenance and finally fraud investigation and analysis. The automatization of the remaining 59% does not yet allow for the emergence of another representative use, although it shows important trends in innovation (3). This indicates that the development of AI is booming and generating an ever greater impact on the business world.

In short, the concept of AI has been established in the world of the digital era and is imposing new challenges for business managers. This document is intended to serve as a brief introduction to the fundamental concepts of the field and some of their uses. It will present the main concepts of the subject and complement them with the lessons from different companies and managers who are already implementing AI in their daily operations.

Even today AI has remained valid and its new definitions tend to unite science and engineering under the idea that each one points to:

John McCarthy Computational and Cognitive Scientist.

Ph.D. in mathematics(1927 – 2011)

Understand and model "intelligent" systems

Build "smart" machines

(Among others)

In the same respect AI has been proposed as an interdisciplinary space that brings together scientific and academic efforts in areas such as:

In 1955, Stanford researcher John McCarthy coined the term AI during the Dartmouth Conference. During this stage its primary mission was also defined. In the original proposal for said conference, McCarthy said that the objective of this discipline would be:

“[…] to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans and improve themselves.

We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer” (4).

EngineeringScience

02 Introduction / What is Artificial Intelligence?

Computation

Mathematics Philosophy

Language

General IA refers to a system that could resolve any general task creatively and imitate human behavior.

Under this type of AI, a system should possess the human cognitive capacities, the general understanding of the experience of our species and the processing speed of machines. Aside from this, one might expect it to evolve exponentially in relation to its knowledge, cognitive capacity and ability to process new data. This scenario

could be considered, in the words of Raymond Kurzweil, a "Singularity" (5): this myth indicates that the human species could be overcome by an AI entity. Kurzweil himself, a former Google Security Director, speculates that an AI system could pass a traditional Turing Test and surpass human intelligence by 2029.

Given that today there is no possibility of reconstructing real simulations of human cognition, it would be impossible to assure the materialization of such assertion. What is evident is the sustained development of AI solutions in various areas of social and economic life.

This type of AI currently exists today. Focused AI is programmed to perform a single task: review the weather, play chess, etc. Their great value is that they solve specific problems of companies, even in real time.

The weakness of these systems is that they are unable to operate outside the particular task for which they are designed. They fail if the task conditions change, even if it is a minimal modification.

For example, facial image recognition technology is very accurate, but would not necessarily serve to solve a problem of recognition of product images.

Some examples of use of focused AI could be: systems that recommend products based on previous purchases or systems that learn to recognize images (for example a cow) from samples (many photos of cows), among others

“Machines with the ability to perform specific tasks and obtain very good results”

“Machines designed to think and function like the

human mind”

FOCUSED ARTIFICIAL INTELLIGENCEPRESENT

NARROW ARTIFICIAL INTELLIGENCEGENERAL ARTIFICIAL INTELLIGENCE

FUTURE

ARTIFICIAL GENERAL INTELLIGENCE

03 Artificial Intelligence / Present and Future

Area of Computer Science pursuing machine imitation of human behavior.

ARTIFICIAL INTELLIGENCE01

02

03

Subset of AI using statistical methods to allow machines to learn a task and keep improving with experience.

MACHINE LEARNING

Subset of ML that uses multilayer neural networks as a mechanism for machines to learn and continuously improve.

DEEP LEARNING

Generally, there is usually a lot of confusion around the concept of AI. A study (6) conducted by MMC Ventures in Europe indicated that more than 40% of 2,830 startups surveyed reported using AI when in fact they did not. It is also very common to observe the indistinct and synonymic use of the terms AI, Machine Learning (ML) and Deep Learning (DL), constituting 3 characteristic and very important terms within the field. The difference between these can be summarized as follows:

1) Artificial Intelligence: area of Computer Science that seeks machine imitation of human behavior and whose support appeals to different learning techniques.

2) Machine Learning: branch of AI that is defined as the scientific study of algorithms and statistical models that allow for the training of a machine to perform a specific task without using particular instructions, but instead based on patterns and inferences (7).

3) Deep Learning: ML method that is based on artificial neural networks arranged in layers, with the purpose of imitating the learning form of the human brain (8). A neural network that allows different ML algorithms to process complex data inputs (9).

*Artificial neural networks (also known as connectionist systems) are a computational model loosely inspired by the behavior observed in their biological counterpart: the brain. It consists of a set of units, called artificial neurons, connected to each other to transmit signals. The input information traverses the neural network (undergoing various operations) and produces certain output values.

04 Artificial Intelligence / Levels of complexity

2) Machine Learning o Automatic Learning  (ML): The great premise on which ML is held is that systems may acquire knowledge directly from data. In order for the machine to achieve learning, algorithms must be used to analyze data, learn from them and then make a prediction linking new data to what has already been learned.

Thus, instead of manually coding software routines with a specific set of instructions, the machine is "trained" from large volumes of data and utilizes algorithms that give it the ability to learn how to perform a specific task.

05 Artificial Intelligence / Levels of complexity

FIRST GENERATION:Based on rules

SECOND GENERATION:Traditional Machine Learning

THIRD GENERATIONDeep learning

1) Rule-Based Systems (SBR) : systems that resolve a problem by following manually programmed procedures, via rules established and programmed by a person (10). The first AI-based programs were of this type. For example: Deep Blue (a supercomputer developed by IBM to play chess).

Currently, these techniques are still used in certain programs that aim to solve low complexity problems within companies. One of their great disadvantages is that as they grow, they become more difficult to maintain and update.

3) Deep Learning (DL): ML branch that imitates the human learning process. DL uses layers of neural networks and algorithms to process data. This has allowed substantial advances when it comes to understanding human speech or visual recognition of objects.

In this case, the data goes through each layer of artificial neural networks, obtaining a result for each. The result of the immediately preceding layer serves as an input to the next layer.

That said, all intermediate layers are called hidden layers. In technical terms, each layer is typically a simple and uniform algorithm that contains a type of activation function (11).

06 Artificial Intelligence / Levels of complexity

f (”King”) - f(”Male”) + f(”Women”) = f(”Queen”)

Indicates that there is a cup in the photo

f (”Santiago”) - f(”Chile”) + f(”Greece”) = f(”Athens”)

Input Layer

HiddenLayer 2

Hidden Layer 1

Output Layer

1

0.99

0.3

0.9-3 122

0.2-10 -39

0.3

The term Big Data is very prevalent in the current digital world. It is said that the computer scientist John Mashey had popularized it, going back to the 90s. In 2016, it was defined that "Big Data represents information assets characterized by such high volumes, speed and variety that require technology and specific analytical methods for its transformation into value" (12) such that the need to develop tools to address such challenges can be inferred.

Likewise, "[...] the complex nature of Big Data is mainly due to the unstructured nature of much of the data generated by modern technologies, such as web logs, radio frequency identification (RFID), sensors embedded in devices, machinery, vehicles, internet searches, social networks like Facebook, laptops, smartphones and other mobile phones, GPS devices and call center records. [...] In this manner for the case of businesses, Big Data must be combined with structured data (usually from a relational database) of a more conventional commercial application, such as an ERP (Enterprise Resource Planning) or CRM (Customer Relationship Management) (13). This will allow organizations to obtain new insights to strengthen both their production and their relationship with the client.

In general, many sources agree that data generation will undergo exponential growth by 2020. Therefore, it is believed that the size of the digital universe will double, at least, every two years. This could mean an increase in the amount of data of up to 50 times between 2010 and 2020, in addition to global growth rates up to 10 times faster than traditional business data (14).

In this scenario, one of the considerable challenges of Big Data is the quality of the data obtained. In response, five principles called "5 V" have been proposed whose function is to demarcate the optimal characteristics of the data:

1.Volume: large volumes of data generated and stored.

2.Speed: speed to generate, store and analyze data.

3.Variety: the set contains different types of data.

4.Truthfulness: the data set is reliable, truthful and real.

5.Value: the analyzed data ends up adding value to the organization.

07 Big Data and Artificial Intelligence

08 Big Data and Artificial Intelligence

These 5 characteristics of Big Data allow companies to recognize that their data sets can be of massive, changing and of a complex nature. This also allows them to deal with the problems related to data extraction, obtaining results that meet highest quality standards.

On the other hand, the convergence of Big Data with AI is what the MIT Sloan Management Review called the most important specific development of the era, whose scope grants companies the opportunity to obtain value from their data and achieve analytical capabilities to succeed in the future (15).In this fashion, it can be deduced that combining Big Data with AI could allow them to acquire incalculable advantages over the competition.

3000

2010 2020

4000

5000

6000

7000

8000

9000

Sensors &Devices

SocialNetworks

VOIP

Business Data

50xincrease

1 Exabyte (EB) = 1.000.000.000.000.000.000 bytes

Volu

me

in E

xaby

tes

to 2020

from2010

90%

We are here

o f t h e

2 Years

world’s datahas been createdin the past

At a high level, we are talking about experts in working with large amounts of data and engineers. While the prior (Data Scientists) need to fully understand the data behind their work, an engineer has the task of building something ” (16).

In summary, it can be said that on one hand a data scientist is a professional capable of treating (harvesting) the data and applying certain techniques, methodologies and procedures to obtain value from them. On the other hand, an AI Engineer should be able to support the construction and maintenance of a software solution based on the knowledge of this area with an emphasis on the creation and maintenance of the ML models designed.

DataScientist

VSAI Engineers Data Scientists

09 AI Engineers vs Data Scientists

One of the most important challenges for the future is to obtain valuable information from large volumes of data, which is in itself related to Big Data. Until not long ago, this task was not obtaining the expected results mainly because the tools and training of data analysts did not allow for it. Faced with this problem, new roles and profiles began to emerge such as Artificial Intelligence Engineers (or Machine Learning) and Data Scientists. It is worthwhile to differentiate between the two, so as not to see them as homologous terms.

Some blogs, such as Andrew Zola's, point out that "there is a lot of confusion around the roles of the Machine Learning Engineer versus the Data Scientist, firstly because both are relatively new. However, by reducing and examining semantics, the distinction becomes clear [...]

40%

By 2022, 7 out of 10 bytes will never see a Datacenter Edged data

Nuclear data

Human Data

30%

60%70%

This reveals that AI can help analyze data which is simultaneously generated, allowing data filter by higher value. It is estimated that by the year 2022, 7 out of 10 bytes will be captured, analyzed and operated under the same solution, the so-called “intelligent edge” and therefore never see a data center, minimizing the time and energy required (18).

10 Data and Artificial Intelligence

In short, it is not enough to have data. We must ensure that these large volumes of information are as closely adherent as possible to 5V, in addition to having professionals specialized in obtaining value from them. In that respect, there are two considerations that should be kept in mind in relation to the data:

1) The data is fundamental for an AI solution. As long as there is high quality data, the solution will yield better results. Monica Rogati (17) proposes a hierarchy of data in which she explains what is strictly necessary to add AI to different systems in companies. At the bottom of that hierarchy is the correct collection of data, according to the formats and defined systems and the related sufficient quantity.

2) The continuous and exponential increase in relation to the generation of data implies in itself that storage costs are evaluated. This must be done with the purpose of not incurring excessive expenses and save only the data that has been highlighted as the most valuable for the organization.

LEARN / OPTIMIZE

SOLUTION

ADD / TAG

EXPLORE / TRANSFORM

MOVEMENT / STORAGE

HARVEST

IADeep

Learning

A/B testingExperimentation

ML Simple algorithms

Analiytics, MetricsSegments, Additional

Features, Training Information

Cleaning, Anomaly Detection, Preparation

Flow of Reliable Data, Pipelines, ETL, Data Storage, Structure and Non-structure

Instrumentalization, Logging, Sensors, External Data, User Generated Content

AI has already conquered much of our daily lives. Some examples are Netflix, which recommends movies according to our preferences as if it were a friend who knows our tastes, or Amazon, which shows us products related to our purchases and interests in the same way that a specialized vendor would. Another example is Waze, which gives us the optimal route to the desired destination such as a co-pilot who assists us on a long journey.

Each of these solutions is based on the use of algorithms that are capable of understanding the historical behavior of a user: what they saw or bought, with the purpose of recommending new options. They can also evaluate in real time what is happening in traffic and the dozens of routes that exist for a destination, with the aim of helping to save driving time. In short: AI is already among us and it's here to stay.

AI preserves the purpose that machines can emulate task resolution like a human mind. Currently, AI is limited to the resolution of specific problems.

Nevertheless, AI has been developed in fields with applications in all areas where the use of language, vision, information analysis, and decision making can be automated and systematized.

In general, the first two of the following three characteristics compose human cognition: reason, speech and emotions.

Artificial Intelligence

Vision

Language Analysis

11 Artificial Intelligence / Application

Each organization should consider the potential impact of AI on its strategy and investigate how this technology could be applied to their business’ problems. In many ways, avoiding the incorporation of AI is equivalent to ignoring the next phase of automation. The great risk that is assumed when not attending to the integration of this technology generates a competitive disadvantage in the organization (19).

The vast majority of managers and organizational decision-makers should begin to evaluate AI incorporation with the purpose of enhancing both their profits and their relationship with the client. This raises the questions: “Where can we incorporate AI? How prepared is my organization to implement AI? How disruptive could this be to my industry?”

According to the PWC analysis (20), by the year 2030 as a result of the accelerated development and use of AI, a 14% increase in world GDP is expected; that is to say the growth equivalent of USD 15.7 billion. The economic impact of AI will be gigantic and driven by:

Productivity gains in companies that automate processes (including the use of robots and autonomous vehicles).

Productivity gains in companies that increase their existing workforce with artificial intelligence technologies (assisted and augmented intelligence).

Increase in Consumer Demand as a result of the product and service availability where AI improves quality and personalizes the experience.

12 Artificial Intelligence / Opportunities and risks

(21)

Healthcare3.7

Providers / Health ServicesPharma / Life Sciences

InsuranceConsumer Health

Automotive3.7

Aftermarker & repairComponent suppliers

Personal mobility as aserviceOEM

Financing

Financial Services

3.3Asset wealth management

Banking & CapitalInsurance

Transportation and logistics

3.2TransportationLogistics

Technology,communications and

entertainment

3.1TechnologyEntertainment, media

y communications

Retail

3.0Consumer productsRetail

Energy

2.2Oil and gasPower and utilities

Manufacturing

2.2Industrial ManufacturingIndustrial products/

raw materials

Sector Subsector Potential AIconsumption impact

Grand Totals 3.1

% Adoption maturity: Near term (0-3 years)% Adoption maturity: Mid term (3-7 years)% Adoption maturity: Long Term (7+ years)

37% 23% 40%

35% 47% 18%

41% 59% 0%

41% 41% 17%

47% 36% 17%

54% 38% 8%

39% 44% 17%

14% 83% 3%

13 Artificial Intelligence / Impact by sector

ArtificialIntelligence

Machine Learning

Techniques and

statistical modeling

MathsThe Output function

is created by human and

parameterizedby data

Algorithms forclustering,

regression, networksneuronal, etc

The Output functionis learned by

machine

Deep LearningAutonomous

intelligentbehaviour

Complex neural networks

CommunicationData Science

AI engineers

TechnologyBusiness Dashboard

Big data, BI &Analytics

14 Artificial Intelligence / Concepts and links

Currently, there is an overload of information and marketing around AI and some of its concepts, such as machine learning. Also, there are more and more commercial applications and companies that offer services related to this technology. In this environment of enthusiasm, the pressure on business leaders is increasing: they must integrate AI into the workflows and decision making of each organization. What does this really mean? In our next installment, "Artificial Intelligence in the real world of business" we will address issues associated with the incorporation of AI in companies and different industries, we will deliver our vision of how to begin its adoption, deepening on issues as relevant as:

Benefits of applying AI in companies.

The challenges and obstacles in its incorporation

Adopt, create or buy?

Strategy, structural and organizational changes

Cases of success and failure to incorporate AI

Examples of AI in specific industries

Suggestions to include AI in the short term.

Together with the above, we will deliver information on the handling of data associated with the different solutions, professionals and talents that are needed to obtain maximum value. Clear descriptions on this, some initiatives and cases for each industry will be delivered, without getting lost under the beacon of false marketing in AI.

We invite you to know the benefits, risks and advantages of incorporating new AI tools in your company

AI is a complex discipline that encompasses many areas and knowledge. As a field in constant development, it is full of debates and discussions starring highly qualified specialists, who address many of the basic concepts presented in this delivery. Currently, thanks to technological advances, AI-based solutions can be accessed without specific knowledge, in a more packaged and focused way for various business problems. In this way, directors and professionals can incorporate AI into their organizations and use these tools.

This advance allowed the explosion of AI in the business environment, which has changed different business models, has modified and improved many internal processes and has allowed the sharp increase in the degree of automation of different tasks. Adapting to this new way of operating as part of the strategic definition and business conception of organizations could lead to the emergence of new market leaders, which could even be previously unknown companies.

However, efficiently implementing AI requires understanding not only its basic concepts but also practical ones: the correct way to adopt it in the organization and its potential impact. In any case, its incorporation is fundamental and almost a requirement of the digital world, especially to make strategic and tactical decisions in the short and medium-term. We can affirm, that we are facing the most impressive technological challenge of our era and we must protect the optimal scenario to obtain the maximum of its benefits.

15 Conclusions

(12) Foote, Keith D; (2017) Dataversity. “A Brief History of Deep Learning”. Recovered from: https://www.dataversity.net/brief-history-deep-learning

(13) De Mauro, Andrea; Greco, Marco; Grimaldi, Michele; (2016) "A Formal definition of Big Data based on its essential Features". Roma, Italia: Emerald Group Publishing Limited.

(14) Powerdata; (n.d) “Big Data: ¿En qué consiste? Su importancia, desafíos y gobernabilidad.” Recovered from: https://www.powerdata.es/big-data

(15) insideBIGDATA; (n.d) “The Exponential Growth of Data”. Recovered from: https://www.powerdata.es/big-data

(17) Zola, Andrew; (2019) Springboard Blog. “Machine Learning Engineer vs. Data Scientist”. Recovered from:https://www.springboard.com/blog/machine-learning-engineer-vs-data-scientist/

(18) Rogati, Monica; (2017) Hackernoon. “The AI Hierarchy of

Needs”. Recovered from: https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

(19) Bresniker, Kirk; (2018) World Economic Forum. “A new era of computing is coming. How can we make sure it is sustainable?”. Recovered from: https://www.weforum.org/agenda/2018/09/end-of-an-era-what-computing-will-look-like-after-moores-law/

(20, 21) S. Rao, Dr. Anand & Verweij, Gerard; (2017) PwC. “Sizing the prize What’s the real value of AI for your business and how can you capitalise?”. Recovered from: https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf

Other links of interest on the subject:

i. Obiols, Albert; (2015) inLab FIB. “¿Qué es un Data Scientiest?”. Universidad Politécnica de Catalunya: Barcelona Tech. Recovered from: https://inlab.fib.upc.edu/es/blog/que-es-un-data-scientist

ii. Berkeley School of Information; (n.d) “What is Data Science?”.

iii. Data Science; (n.d) En Wikipedia. Recovered : May 20th 2019 from: https://es.wikipedia.org/wiki/Ciencia_de_datos

(1) Jackson Jr, Philip C; (1974) “Introduction to Artificial Intelligence”. New York: Dover Publications, Inc.

(2) Hippold, Sarah; (2019) Smarter with Gartner. “5 AI Myths debunked”. Recovered from: www.gartner.com/smarterwithgartner/5-ai-myths-debunked

(3) D'Aquila, M. & Shirer, M; (2018) International Data Corporation (IDC). “Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $77.6 Billion in 2022, According to New IDC Spending Guide”. Recovered from: www.idc.com/getdoc.jsp?containerId=prUS44291818

(4) McCarthy, J; Minsky, M. L; Rochester, N; Shannon, C. E; (1955) “A proposal for the Dartmouth Summer Research Project on Artificial Intelligence”. Paper presentado en la Conferencia de Darmouth. Hanover, Nuevo Hampshire. Recovered from: http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf

(5) Kurzweil, Ray; (2006) “The Singularity Is Near”. New York: Vikings Books.

(6) Educba; (n.d) “Artificial Intelligence vs Machine Learning vs Deep Learning Comparison Table”. Recovered from: https://www.educba.com/artificial-intelligence-vs-machine-learning-vs-deep-learning

(7) Kelnar, David; (2019) Medium. “The State of AI 2019: Divergence”. MCC Ventures en conjunto con Barclays. Recovered from: https://www.stateofai2019.com/

(8) Machine Learning; (n.d) En Wikipedia. Recovered : May 20th 2019 from: https://en.wikipedia.org/wiki/Machine_learning

(9) Artificial Neural Network; (n.d) Wikipedia. Recovered : May 20th 2019 from: https://en.wikipedia.org/wiki/Artificial_neural_network

(10) Deep Learning; (n.d) En Wikipedia. Recovered : May 20th 2019 from: https://en.wikipedia.org/wiki/Deep_learning

(11) Giarratano, Joseph C. & Riley, Gary D; (2004) “Expert Systems: Principles and Programming”, Fourth Edition EEUU: Course Technology Inc.

16 References

Conoceanastasia