AI (Artificial Intelligence) is the simulation of human intelligence processes by machines, especially computer systems. Particular applications of AI include expert systems, speech recognition and machine vision. It (AI) is a branch of computer engineering, designed to create machines that behave like humans. Although AI has come so far in recent years, it is still missing essential pieces of human behavior such as emotional behavior, identifying objects and handling them smoothly like a human. Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.
Over the years, Business Intelligence has become more and more technology driven. Today, in most organisations, business cases are prepared to check whether every aspect of any deal is in line with the present business and economic standards. But as Business Intelligence matures, it becomes increasingly market-driven, and technology slowly becomes secondary to the processes and applications.
Most organisations nowadays consider Business Intelligence as an essential factor that goes hand in hand with strategic management, innovation management, change management and knowledge management. Because of Business Intelligence, knowledge and information are applied in the business processes more efficiently thus allowing organisations to react more quickly.
We will be able to see the following patterns in the coming years concerning Business Intelligence: More Personalisation: Smarter systems will result in organisations getting highly personalised reports for themselves. Traditionally, reports are generated and used by multiple users because of the difficulty and amount of resources required to personalise them. This problem will be non-existent in the future. More Entity-centric: Entity identification today isn’t used often and is considered as a high-end function. It is mostly used only within bank fraud detection and government intelligence. Since an increasing amount of data continues to go online, Entity will become more of an integral part of Business Intelligence. Finding Relevant Data: Due to the increasing digitisation, you won’t have to search for relevant data anymore. Business Intelligence will bring all necessary data to your attention. The Authenticity of Data: The authenticity of data will become more and more important down the line. This is because as the software becomes more efficient and provides us with better data on time, the ease with which we can act on it also increases. This becomes a problem when we consider how organisations and government are going to be using this data to power policy and law enforcement.
Having the erroneous information can result in false arrests, bureaucratic problems and can even mean the difference between life and death if we consider military applications.
When it comes to Business Analytics, many advances have already been made in the sector using AI, Natural Language processing, etc. Unlike the current applications which allow us to visualise and cluster data and make simple forecasts, the next generation of augmented analytics automates the whole process and gives us actionable predictive guidance.
Augmented analytics can sort all the data given to it and use this data to decipher hidden patterns and build models.
The reasons why we need organisations analytics is due to the shortcomings of conventional methods of data analytics, which are as follows:
They take a lot of time to produce the desired results.
Predictions made by humans are undoubtedly prone to more errors.
Implementation is expensive and not cost-effective
Business owners and organisations are trying to overcome the above problems, and augmented analytics is the solution. The application of expanded analytics is set to transform the way businesses work in the near future completely.
[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]The world of data science is an interdisciplinary one involving mathematical and statistical skills along with high computational and programming knowledge and also the ability to understand business trends through large databases. The job of the data scientist is to analyse a massive amount of data and interpret them in order to help the organization make suitable decisions based on data prediction. Machine Learning being a newer technology therefore, can be put in the same plate as the data scientist based on their job importance. The field of machine learning involves the usage of big data and there analysis, narrowing them down through algorithms. Such values are created which can be put to further substantial use. The task is often repetitive and hence, the machines are taught to “learn” their work. In fact, the traditional hit and trial method of data analysis is becoming outdated and impractical as the need to interpret big data arises. Therefore, a data scientist can really not move ahead in the current organizational world if the person lacks the knowledge of Machine learning among several other skills.
Algorithms are an essential part of any ML training, and they serve to be necessary for data scientists. The data scientist has several overlapping skills with the machine learning expert namely adeptness in basics of computer, a credible knowledge of programming in several languages, through exposure to statistics and also skills in data modelling and data evaluation among others. As such machine learning can comfortably fit into the oeuvre of data science. The knowledge of the various techniques of machine learning- supervised and unsupervised, all come into being necessary for the data scientist. Nonetheless, data science has a higher perspective to look into more significant matters of applying the entire process into practical usage and hence involving the incorporation of experience in organisational trades. Ultimately data science involves parental branches like data analytics, software engineering, and business analytics and more including machine learning. Data science along with machine learning skills is quite in demand by organisations and gives you the very high prospect of improvement in your field.
Henceforth, machine learning is compulsorily added as a component of data scientist training. There are basics of ML which was already present in the course, but as the field has evolved, the presence of ML is more explicit. The filtering of data by algorithmic techniques comes in very handy for the data scientist to work on a specified collection of data. Classification of data is a crucial step for calculations and being able to detect essential predictions. Machine learning at this moment forms an explanatory core for the data scientist through which one can explain the successive discoveries. One can cite examples of the application of machine learning in the job of data scientist. A data scientist works at several organisational firms to analyse customer credibility based on the previous collection of data. The procedure could involve the customer’s transaction data and ratings per view. Next step would be to use an ML algorithm to work a prediction using any of the supervised techniques. One may use the decision tree to conclude that the customer was not creditworthy. The entire process can be showcased via using a visualisation of the decision tree with a stable structure which would be easy to explain.
Machine learning is a substantial extension of the field of data science such that it not only exemplifies the entire procedure but also makes further provisions for data analysis and data filtering. If you are well trained in ML, you can opt for a job as a data scientist.[/vc_column_text][/vc_column][/vc_row]
Artificial intelligence (AI) and Machine learning have a remarkable potential to accelerate, simplify, and improve many aspects of our everyday lives. Early results have simultaneously created colossal excitement and demonstrated its frightening potential. In the following infographics, we discuss the developing interest in Artificial Intelligence (AI) and Machine Learning in organisations. The data has been gathered by asking different questions to IT professionals of various levels.
The outcome as is as follows:
The era of Big Data has created a talent gap for people who can pull actionable insights out of raw data. The data scientist – called “the sexiest job of the 21st century” by Harvard Business Review – is in demand, with a 5000% jump in job posts between 2013-2014. In India, the average salary for these sought-after scientists is around 650000 INR.
The advent of the internet, artificial intelligence, technology and vast amounts of data in our everyday lives has made the process of data visualisation, its use in the various means of buying, selling, post-selling and its presentation skills one of the prime tools in data analytics. The display itself has used every new device, beginning from charts, graphs to animation, virtual reality, augmented reality and everything in between to evolve in a chimerical fashion to make the process of presenting data at the opportune moment in an engaging, communicative manner the differentiator.
Should one compare the free and open-source alternatives with the paid data visualisation tools? The answer should be ‘yes’ because, in a world where speed, accuracy, flexible and robust solutions count, and data security could be an area of fatality, they best evolve with practice, use, development and customer retention which costs.
Let’s do a quick review of some of the visualisation tools and why they are regarded as the top ones.
Tableau
Tableau handles significant and rapidly changing data with ease. Integrating Hadoop, My SQL, SAP, Amazon AWS, and Teradata with AI and machine learning capabilities tweaks the presentation into dynamic visualisation which is easy to understand.
QlikView
Some of the most promising features are that it has a customizable set-up, a very widely used feature range, and a clutter-free interface. It does suffer in taking a little more time for full use of its potential but can be used for data discovery and exploration in conjunction with Qliksense for BI solutions with excellent reporting, analytics and intelligence capabilities.
Fusion Charts
Widely used over 90 chart types which integrate across platforms and frameworks give it flexibility. FusionCharts significant improvement is its use instead of starting a new visualisation from scratch by just using the “live” example templates and plugging in their data sources as needed.
Highcharts
Its focus on cross-browser support makes it an excellent choice when fast and flexible solutions with minimum need for specialist data visualisation training is needed.
Datawrapper
The package is widely used by media organisations to create charts, upload csv and present statistics in straightforward charts, maps etc. that allow quick embedding into reports.
Plotly and Sisense also find mention among the top few. In selecting the best data visualisation tools for 2018, the following devices have earned their place with Sisense and Tableau being apparent frontrunners.
Zoho Reports
With seamless integration, Zoho is user-friendly, has automatic report generation and impressive support availability. It is a business intelligence and analytics software that helps create insightful dashboards and data visualisations with reports on project status, burn down charts, utilisation of time, planned reports vs actual reports and so much more.
Domo
Domo offers storage, great sharing features and a range of connectors but is a little short when it comes to its interface and learning curve.
Microsoft Power BI
Known for its superior capabilities, compatibility and easy usability, Power BI has a horde of pluses including data preparation, data discovery and interactive dashboards. Users also can load custom visualisations. However, it only falters because the desktop and web versions divide the data prep tools with the refresh cycle being limited.
Google Analytics
This exceptional platform for the website and mobile app analytics relies on third parties for training and has way too much automation in customer support.
Chartio, SAP Analytics Cloud, IBM Watson Analytics and the evergreen Salesforce Einstein Analytics platform are also climbing the charts steadily.
Data is an invaluable resource and managing it, is a tricky task for business solutions. Larger enterprises can afford data analysis teams, but smaller firms rely on data visualisation tools. If one uses technology, infrastructure, big data, and visualisation tools, it is possible to streamline operations, internally to become leaner and more efficient. Last but not the least, it helps to understand your customers and earn their loyalty. References:
https://in.pcmag.com/cloud-services/106561/guide/the-best-data-visualization-tools-of-2018
https://www.forbes.com/sites/bernardmarr/2017/07/20/the-7-best-data-visualization-tools-in-2017/#111b7fdd6c30
Success in most enterprises is reliant on the IT organization and technology to help in business operations, using big databases and predictive analysis for forecasting trends and finding solutions for real-time decisions. Tableau is one of the best tools available with its unique features like a built-in dashboard, requiring no R scripting or other scripting and being able to import your data in varied formats onto the panel.
Pillars of Predictive Analysis
The main components of Predictive Analysis are monitoring at any given moment of Big Data, the understanding of data analytics and effective utilization of data across the enterprise. Tableau can provide specific views of small events or co-relate information to present trends and forecasts in real-time. Tableau can thus ensure efficiency in allocating resources and increasing organizational effectiveness. The V8 version of Tableau allows you to set alerts and saves you and your clients from data disruptions and system downtime. Further, the Tableau package provides for security, improved governance, and reliable, flexible and robust solutions applicable across the entire organization by using data analytics, predictive analysis, and machine learning. Radically Tableau’s capacity as the best tool for predictive analysis reduces the time it takes to connect to your data, visualize, analyze, and ultimately find business solutions as in the New York City Health Solutions case.
Benefits of Predictive Analysis in Tableau
The takeaway illustrates that Predictive Analysis in Tableau has considerable benefits enumerated below.
See all the facets of your IT organization
Monitor in real-time – the utilization
Allocate resources efficiently
Deploy securely across the enterprise
Implementation Of Predictive Analysis Using Tableau
IT Architecture Building
The architecture of your analytics decides your data flowchart, where and when it is processed, which database to use, who will monitor the insights, how it is secured and most importantly how it will impact business goals. Data is the very life breath of an organization and its timely use in predicting trends and forecasts invaluable to business growth.
Become an Algorithmic business with Tableau
The algorithm can work, understand and process the data to give you gainful insights and trends. However, using these is the crux of the matter. Of course, the Tableau package is the most superior tool for predictive analysis and with its excellent features like Examples:
Online retail segment benefits significantly from data analytics in real time by catering to clients based on their purchase history, browsing habits and other demographics.
The housing sector can predict trends, price products right, increase buying, selling, customer acquisition and retention.
Information can already be at your fingertips, to empower you and your organisation. However, this requires to be a data-driven business implementation of choices.
Align and Prioritize Analytics
An example of prioritizing is the Intel Corporation who evaluates analytics to meet and align with enterprise goals and grow trust and clients. The criteria they use to assess potential projects include
Executive sponsorship. Big Data analytics is limited by the stake holder’s involvement and dedication to business goals across all levels of the organization.
Finding and dealing with the right problem. Data and predictive analytics have no preferences and can quickly become a liability if the identification of the right insight aligned with business targets and growth is not identified and prioritized across the entire board.
Data needs to be used right. Quality of data and API availability are essential criteria that impact the feasibility and value of the project.
Resources. The skill availability, choice and use of tools, and processing power will decide how quickly the project gets underway and ends before deadlines.
Time to delivery
Projected benefits.
Conceptual use and Change Management
Big data and analytics of it need to serve the process. To prove the concept is to use the results effectively and, in this segment, it is entirely up to the organization to implement. Caution your data may grow, and your business will grow. Technology shall evolve and change. Hence at some future point changes to infrastructure may be needed.
Indeed the advent of the internet, artificial intelligence, technology and vast amounts of data in our everyday lives has made the process of predictive analytics and data visualization, its use in the various operations of buying, selling, after-sales and its presentation skills one of the prime tools in data analytics using Tableau.
Impact in Machine Learning and Artificial Intelligence on Real Estate and Trends!
Have you ever been on the lookout to buy a house or an apartment and found yourself buried in heaps of redundant information? Your dreams of owning property were either forced out the window or grew even more complicated thanks to inadequate information. Well, thankfully this is effectively becoming a thing of the past with the entrance of AI into the world of real estate.
Artificial Intelligence is finally taking over every arena and domain in the world and comes as a welcome relief rather than a cause of alarm amongst us. Real estate is relatively late to the game, consequently, only a few elements of the sector are currently benefiting from machine learning.
What Is Machine Learning?
Machine learning is mostly a computer algorithm that assimilates every ounce of data it is fed, analyses it, adapts to and evolves with this information. Essentially a program uses all of this to create a better version of itself. If you were to look for homes on a website or continuously search for a set of parameters, machine learning will pick up on this and tailor searches to make them more precise and even send recommendations on related listings your way. You are likely to not only have a large number of choices you will be pleased with and a quick search that makes you a homeowner in no time.
Applications in Real Estate
A real estate company can watch their sales numbers explode thanks to AI-driven programs that bring the right customers to them. If you employ this software, you could soon be up for the title of ‘sales executive of the month’ thanks to the number of customers coming your way. Algorithms also improve upon sales campaigns and perfect the entire marketing sales process to bring tenant and landlord together.
With insights, you learn how to ensure listings can become more attractive to search engines. A seller could help a potential customer clarify all their queries through a bot that learns from each question from various customers. However, you run the risk of a bot being so robotic that it comes across as rude or frustrates a customer by repeating the answer a script provides it. While AI is enhancing enough to do away with this drawback slowly.
Once you own your dream home, you will realize machine learning comes into play, mainly with property management. Automation plays a big part in ensuring the lighting, temperature and in general, the HVAC systems of a building are keeping everyone in it satisfied, while enabling the owner to manage bills more effectively.
The most exciting realm of AI for real estate lies in virtual or augmented reality.
They currently exist in simpler forms such as 360° views of a home, so you get a better feel for your home before you even buy it. However, this function is available in a limited capacity with researchers still sorting the multiple bugs that accompany it.
Irrespective of its pros and cons, machine learning, is here to stay inside and outside the world of real estate. Whether you actively use it to better your real estate experience or not, it has a part to play in improving every element of the process.
Artificial Intelligence is a coherent system calibrated and fabricated in such a fashion that it resembles an epitome of analogy with that of natural Intelligence with an escalated structure and functionality where the room for evolution eclipses the natural intelligence by a cosmic margin. Germination and Evolution of Artificial Intelligence is a constant that establishes and sustains through a fundamental variable that is technology. As AI is disseminating the modern era through its nurtured growth there seems to exist some congruity in its caricature with respect to natural intelligence which needs to be comprehended to get the crux of this aborning technology. Natural Language Processing (NLP) is one such approach used by the AI technology which renders the AI with a competency to interact with other foreign intelligent system using a common language so as to amalgamate and match the counterpart on a same altitude and platform.
Artificial Intelligence has garnered technological advancements such that harnessing said attributes becomes a convenient process in the future. Hence NLP process is designed with such a foundation that would enable and aid a robot to comprehend the language postulated by the user. To further cognize NLP, it certainly becomes imperative to assimilate components of NLP that aid in sufficing such functionalities.
Natural Language Understanding (NLU)
This domain deals with mapping a language into its comprehensive structure which can be assimilated conveniently. This rendition and mapping integrate a language with the desired language that could be deciphered by the machine.
Natural Language Generation (NLG)
NLP is not just a process that catalyzes the accumulation of words that a machine understands to get a certain task done, it is, on the other hand, a process that provides a meaningful structure to the word which than machine follows in the form of language. This meaning is exhibited by NLG which basically comprises three phases.
Text Planning – Extraction of useful information out of the whole bank is introduced under this spectrum.
Sentence Planning – This initiates the most crucial process where a proper meaning is formulated out of chosen words so as to form a phrase and an abstract structure which the machine takes into account to follow a certain paradigm.
Text Realization – This phase marks the final script and formulation of a systematic structure is aggregated in this phase. This phase finally lends meaning to a command in the form of a language for the machine to follow.
NLP portrays a functionality and utility in a system by transitioning through various stages which fabricates the whole process into one spectrum. Hence bracketing those transitions is of critical importance as and when the process proceeds.
Lexical Analysis
Useful data is mined from certain sources which exist in an unstructured format in large chunks and pose a shallowness in comprehension by the machine. Hence to make the study easier the large chunk of data is segregated in the form of paragraphs, sentences and words which would further me oriented in a more orderly fashion so as to designate a meaningful outcome.
Syntactic Analysis (Parsing)
A Mere collection of words would not suffice a meaning to the accumulated data resulting in the desired output and a meaningful analysis of grammatical forefront is also of immense importance so that a proper communication is established for a smooth outcome.
Semantic Analysis
Post checking the sanctity of grammatical soundness in acquired data set further analysis is carried out to monitor the contradictory phrases and bifurcated them clearly.
Disclosure Integration
Final integration of sentence is carried out so that subsequent meaning arises out of sentences for the machine to assimilate.
Artificial Intelligence or AI is a concept that has been much talked about in the recent past. As the name suggests, Artificial Intelligence is not real, in the sense that it is simulated. Intelligence is a broad term that can mean different things. Generally, intelligence can be defined in different spectrums, ranging from logic to problem-solving abilities. Thus, Artificial Intelligence is a subfield of computer science that simulates machines and software to mimic the human cognitive functions. This leads to the production of machinery that observes, analyzes and outputs data just as the human brain would.
Here is a brief history of Artificial Intelligence that highlights how the concept has grown leaps and bounds in the recent years. Ancient History: A fascinating fact about artificial intelligence is that it has been incorporated since as early as the 4th century B.C. According to Greek mythology, a blacksmith called Hephaestus manufactured mechanical servants to help him out with his work. With time, many models and toys were constructed with the same underlying principles. Examples of such models include the Archytas of Tarentum and the syllogistic logic model by Aristotle. Also Read : The Promises of Artificial Intelligence: Introduction History of AI since the last 100 years:
Rossum’s Universal Robots
In the year 1920, a Czech writer named Karel Capek wrote and directed a sci-fi play. This play introduced the concept of robots known as Rossum’s Universal Robots (R.U.R) The play depicted artificial people who are similar to clones in today’s day and age. This gave researchers an idea about Artificial Intelligence and Robotics and thus threw light on the importance of AI in society and research.
The Alan Turing Machine
Alan Turing, a renowned scientist, introduced a model called the Turing Machine. This machine is an abstract one that constructs the logic of any given algorithm. Up until today, the Turing Machine holds a special place in the field of Computer Science. Shortly after the World War 2, Turing introduced his famous Turing Test. This test was created to determine the intelligence of any machine. The test used one machine and one person. Both the machine as well as the person would communicate through a natural language. If a second person who is overhearing the conversation cannot differentiate between the person and the machine, then the machine is said to be intelligent.
The Dartmouth Conference
In the year 1956, the first recorded Artificial Intelligence workshop was held. This marked the start of research in the field where researchers from MIT, CMU and IBM met together and brainstormed ideas related to the concept. According to them, creating artificially intelligent machines will ensure that the work to be done by man will significantly reduce with time.
Expert Systems
Research in AI significantly decreased in the subsequent years due to lack of enthusiasm and funding by the US and British Governments. Thus, during the early 1970s, AI was a concept that was neglected. However, after a few years, expert systems were introduced. These systems are programs pertaining to a particular domain that answers as well as solves any problems posed to it. Thus, they emulate an expert in a particular domain and solve the posed problem according to certain rules that are set. The expert program used a knowledge engine that represented facts and rules with respect to a certain topic while the inference engine applied these represented facts to new facts.
Shortly after, in the 1990s, AI research suffered another setback due to lack of funding. 21st Century: The 21st century saw the growth of Artificial Intelligence that extended to Machine Learning and Big Data Analysis. Related Article:
