Dr. O. Aly
Computer Science

The purpose of this discussion is to discuss the influence of artificial intelligence on big data analytics. As discussed in the previous discussion, Big Data empowers artificial intelligence.  This discussion is about the impact of artificial intelligence in the Big Data Analytics domain.  The discussion begins with artificial intelligence building blocks and big data building blocks, following by the impact of the artificial intelligence in the BDA. 

Artificial Intelligence Building Blocks and Their Impact on BDA

Understanding the building blocks of AI could help understand the impact of AI on BDA.  Various reports and studies have identified various building blocks for AI.  Four building blocks have been identified  In (Chibuk, 2018), four building blocks that are expected to shape the next stage of AI.  The computation methodology is the first building block of AI.  This component is structured in a way to improve the computers move from binary to infinite connections. The storage of the information is the second building block of AI improving storing and accessing data in the more efficient form.  Brain-computer interface is the third building block of AI, through which the human minds would speak silently with a computer, and our thought would turn into actions.  The mathematics and algorithms form the last building block of AI to include advanced mathematics called capsule network and having networks to teach each other based on rules defined (Chibuk, 2018). 

(Rao, 2017) has identified five fundamental building blocks for AI in the banking sector, while they can be easily applicable to other sectors. Machine learning (ML) is the first component of AI in banking where the software can learn on its own without being programmed and adjust its algorithms to respond to new insights. The data mining algorithms hand over findings to a human for further work, while machine learning can act on its own (Rao, 2017).  The financial and banking industry can benefit from machine learning for fraud detection, security settlement and alike (Rao, 2017).  The deep learning (DL) is another building block of AI in the banking industry (Rao, 2017).  DL can leverage a hierarchy of artificial neural networks, similar to the human brain to do its job.  DL mimics the human brain to perform non-linear deductions, unlike the linearly traditional programs (Rao, 2017).  DL can produce better decisions by factoring learning from previous transactions or interactions to conclude (Rao, 2017).  Example of DL is the collected information about customers and their behaviors from social networks, from which their likes and preferences can be inferred, and financial institutions can utilize this insight to make contextual, relevant offers to those customers in real-time (Rao, 2017).   Natural language process (NLP) is the third building block for AI in banking (Rao, 2017).  NLP is a key building block in AI to help computers learn, analyze and understand human language (Rao, 2017).  NLP can be used to organize and structure knowledge in order to answer queries, translate content from one language to another, recognize people by their speech, mine text, and perform sentiment analysis (Rao, 2017). The natural language generation (NLG) is another essential building block in AI, which can help computers analyze, understand, and make sense of human language (Rao, 2017).  It can help converse and interact intelligently with humans (Rao, 2017).  NLG can transform raw data into a narrative, which banks such as Credit Suisse are using to generate portfolio review (Rao, 2017).  Visual recognition is the last component of AI which help recognize images and their content (Rao, 2017). It uses DL to perform its role of finding faces, tagging images, identifying the components of visuals, and picking out similar images from a large dataset (Rao, 2017). Various banks such as Australia’s Westpac is using this technology to allow customers to activate their new card from their smartphone camera, and Bank of America, Citibank, Wells Fargo, and TD Bank are using this technology of visual recognition to allow customers to deposit checks remotely via mobile app (Rao, 2017).

(Gerbert, Hecker, Steinhäuser, & Ruwolt, 2017) have identified ten building blocks for AI.  They have suggested that the simplest AI use cases often consist of a single building block. However, they often evolve to combine two or more blocks over time (Gerbert et al., 2017).  The machine vision is one of the building blocks of AI. The machine vision building block of AI is the classification and tracking of real-world objects based on visual, x-ray, laser or other signals.  The quality of machine vision depends on the labels of a large number of reference images which is performed by a human (Gerbert et al., 2017).  Video-based computer vision is anticipated to recognize actions and predict motions within the next five years (Gerbert et al., 2017).  The speech recognition is another building block which involves the transformation of auditory signals into text (Gerbert et al., 2017).  Siri and Alexa can identify most words in a general vocabulary, but as vocabulary becomes specific, tailored programs such as the PowerScribe of Nuance for radiologist will be needed (Gerbert et al., 2017).  Information processing building block of AI involves searching billions of documents or constructing basic knowledge graphs identifying relationships in text.  This building block is closely related to NLP, which is also identified as another building block of AI (Gerbert et al., 2017).  NLP can provide basic summaries of text and infer intent in some instances (Gerbert et al., 2017). Learning from data is another component of AI, which is a machine learning and able to predict values or classify information based on historical data (Gerbert et al., 2017).  While ML is an element in AI building blocks of machine vision and NLP, it is also a separate building block of AI (Gerbert et al., 2017).  Other building blocks of AI include the planning and exploring agents that can help identify the best sequence of actions to achieve certain goals.  Self-driving cars rely on this building clock for navigation (Gerbert et al., 2017).  The image generation is another building block of AI, which is the opposite of machine vision block, as it creates images based on models.  Speech generation is another building block of AI which covers both data-based text generation and text-based speech synthesis. The handling and control building block of AI refers to interactions with real-world objects (Gerbert et al., 2017). The navigating and movement building block of AI covers the ways where robots move through a given physical environment. The self-driving cars and drones do well with their wheels and rotors.  However, walking on legs especially a single pair of legs is challenging (Gerbert et al., 2017).

Artificial Intelligence (AI) and machine learning (ML) have observed an increasing trend across industries, and public sector (Brook, 2018).  Such increasing trend plays a significant role in the digital world (Brook, 2018).  This increasing trend is driven by the customer-centric view of data involving use data as part of the product or service (Brook, 2018). The customer-centric model assumes data enrichment with data from multiple sources, and the data is divided into real-time data and historical data (Brook, 2018).  Businesses build a trust relationship with customers, where data is becoming the central model for many consumer services such as Amazon, and Facebook (Brook, 2018).   The data value increases over time (Brook, 2018).  The impact of machine learning and artificial intelligence have driven the need for “corporate memory” to be rapidly adopted in organizations.  (Brook, 2018) have suggested organizations implement loosely coupled data silos and data lake which can contribute to the corporate memory and the super-fast data usage in the age of AI-driven data usage.  Various examples of AL and ML impact on BDA and the value of data over time include Coca-Cola’s global market and extensive product list, IBM’s machine learning system Watson, GE Power using BD, ML, and internet of things (IoT) to build internet of energy (Marr, 2018).  Figure 1 shows the impact of AI and ML on Big Data Analytics and the value of the data over time.

Figure 1.  Impact of AI and ML on BDA and the Value of Data Overtime (Brook, 2018).

AI is anticipated to be the most dominant factor that will have a disruptive impact on organizations and businesses (Hansen, 2017).  (Mills, 2018) has suggested that organizations need to embrace BD and AI to help their businesses.  EMC survey has shown that 69% of information technology decision-makers in New Zealand believe that BDA is critical to their business strategy, and 41% already incorporated BD into the everyday business decision (Henderson, 2015).     

The application of AI to BDA can assist businesses and organizations to detect a correlation between factors humans cannot perceive (Henderson, 2015).  It can allow organizations to deal with the speed of the information change today in the business world (Henderson, 2015).   AI can help organization add a level of intelligence to their BDA to understand complex issues better quicker than humans can in the absence of AI (Henderson, 2015).  AI can also serve to fill the gap left by not having enough data analysts available (Henderson, 2015).  AI can also reveal insights that can lead to novel solutions to existing problems or even uncover issues that are not previously known (Henderson, 2015).  A good example of AI impact on BDA is the AI-powered BDA in Canada which is used to identify patterns in the vital signs of premature babies that can be used in the early detection of life-threatening infections.  Figure 2 shows AI and BD working together for better analytics and better insight. 

Figure 2:  Artificial Intelligence and Big Data (Hansen, 2017).

Conclusion

This assignment has discussed the impact of artificial intelligence (AI) on Big Data Analytics (BDA).  It began with the identification of the building blocks of the AI and the impact of each building block on BDA.  BDA has an essential impact on AI as it empowers it, and AI has a crucial role in BDA as demonstrated and proven in various fields especially in the healthcare and financial industries.  The researcher would like to summarize this relationship between AI and BDA in a single statement: “AI without BDA is lame, and BDA without AI is blind.”  

References

Brook, P. (2018). Trends in Big Data and Artificial Intelligence Data

Chibuk, J. D. (2018). Four Building Blocks for a General AI.

Gerbert, P., Hecker, M., Steinhäuser, S., & Ruwolt, P. (2017). The Building Blocks of Artificial Intelligence.

Hansen, S. (2017). How Big Data Is Empowering AI and Machine Learning?

Henderson, J. (2015). Insight: What role does Artificial Intelligence Play in Big Data?  What are the links between artificial intelligence and Big Data?

Marr, B. (2018). 27 Incredible Examples Of AI And Machine Learning In Practice.

Mills, T. (2018). Eight Ways Big Data And AI Are Changing The Business World.

Rao, S. (2017). The Five Fundamental Building Blocks for Artificial Intelligence in Banking.

Dr. O. Aly
Computer Science

The purpose of this discussion is to address whether the combination of Big Data and Artificial Intelligence is significant to any industry.  The discussion also provides an example where Artificial Intelligence has been used and applied successfully.  The chosen sector for such the use of AI is the health care.    

The Significance of Big Data and Artificial Intelligence Integration

            As discussed in U4-DB2, Big Data empowers artificial intelligence.  Thus, there is no doubt about the benefits and advantages of utilizing Big Data in artificial intelligence for businesses.  However, in this discussion, the question is whether the significance of their combination in any industry or specific industries only. 

            McKinsey Global Institute reported in 2011 that not all industries are created equal when parsing the benefits of Big Data (Brown, Chui, & Manyika, 2011).  The report has indicated that although Big Data is changing the game for virtually every sector, it favors some companies and industries over the others, especially in the early stages of the adoption.  McKinsey has also reported in (Manyika et al., 2011) five domains that could take advantages of the transformative potential of Big Data. These domains include for U.S. healthcare, retail, and public sector administration, retail for European Union, and personal location data globally.  Figure 1 illustrates the value of Big Data significant financial value across sectors.

Figure 1.  Big Data Financial Value Across Sectors (Manyika et al., 2011).

            Thus, the value of Big Data Analytics is tremendous already for almost every business, and the value varies from one sector to another.  The combination of Big Data and artificial intelligence is good for innovation (Bean, 2018; Seamans, 2017).  There is no limit for innovation for any business.  Figure 2 shows the 19-year Go player Ke Jie reacts during the second match against Google’s artificial intelligence program AlphaGo in Wuzhen. 

Figure 2.  19-year old Ke Jie Reacts During the Second Match Against Google’s Artificial Intelligence Program AlphaGo (Seamans, 2017).

If the combination of Big Data and artificial intelligence is good for innovation, then, logically every organization and every sector need innovations to survive the competition.  In the survey conducted by NewVantage Partner, 97.2% of the executive decision-makers have reported that their companies are investing in building or launching Big Data and Artificial Intelligence initiatives (Bean, 2018; Patrizio, 2018).  It is also worth noting that 76.5% of the executives have indicated that the availability of Big Data is empowering AI and cognitive initiatives within their organizations (Bean, 2018).  The same survey has also shown 93% of the executives have identified artificial intelligence as the disruptive technology and their organizations are investing in for the future.  This result shows that a common consensus among the executives that organizations must leverage cognitive technologies to compete in an increasingly disruptive period (Bean, 2018). 

AI Application Example in the Health Care Industry

Since healthcare industry has been identified in various research studies about its great benefits from Big Data and artificial intelligence, this sector is chosen as an example of the application of both BD and AI for this discussion. AI is becoming a transformational force in healthcare (Bresnick, 2018).  The healthcare industry has almost endless opportunities to apply technologies such as Big Data and AI to deploy more precise and impactful interventions at the right time in the care of patients (Bresnick, 2018).

Harvard Business Review (HBR) has indicated that 121 health AI and machine learning companies raised $2.7 billion in 206 deals between 2011 and 2017 (Kalis, Collier, & Fu, 2018).  HBR has examined ten promising artificial intelligence applications in healthcare (Kalis et al., 2018).  The findings have shown that the application of AI could create up to $150 billion in annual savings for U.S. health care by 2026 (Kalis et al., 2018).   The investigation has also shown that AI currently creates the most value in assisting the frontline clinicians to be more productive and in making back-end processes more efficient, but not yet in making clinical decisions or improving clinical outcomes (Kalis et al., 2018).  Figure 3 shows the ten AI applications that could change health care.

Figure 3.  Ten Application of AI That Could Change Health Care (Kalis et al., 2018).

Conclusion

            In conclusion, the combination of Big Data and Artificial Intelligence drives innovations for all sectors. Every sector and every business need to innovate to maintain a competitive edge.  Some sectors are leading in taking the advantages of this combination of BD and AI more than others.  Health care is an excellent example of employing artificial intelligence.  However, the application of the AI has its most value on three main areas only of AI-assisted surgery, virtual nurse, administrative workflow.  The use of AI in other areas in healthcare is still in infant stages and will take time until it establishes its root and witness the great benefits of AI application (Kalis et al., 2018).

References

Bean, R. (2018). How Big Data and AI Are Driving Business Innovation in 2018. Retrieved from https://sloanreview.mit.edu/article/how-big-data-and-ai-are-driving-business-innovation-in-2018/.

Bresnick, J. (2018). Top 12 Ways Artificial Intelligence Will Impact Healthcare. Retrieved from https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare.

Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24-35.

Kalis, B., Collier, M., & Fu, R. (2018). 10 Promising AI Applications in Health Care. Retrieved from https://hbr.org/2018/05/10-promising-ai-applications-in-health-care, Harvard Business Review.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.

Patrizio, A. (2018). Big Data vs. Artificial Intelligence.

Seamans, R. (2017). Artificial Intelligence And Big Data: Good For Innovation?

Dr. O. Aly
Computer Science

The purpose of this discussion is to discuss the future impact of Big Data Analytics for Artificial Intelligence.  The discussion will also provide an example of the AI use in Big Data generation and analysis.  The discussion begins with artificial intelligence, followed by an advanced level of big data analysis.  The impact of the Big Data (BD) on the artificial intelligence is also discussed addressing various examples showing how artificial intelligence is empowered by BD.

Artificial Intelligence

Artificial Intelligence (AI) has eight definitions laid out across two dimensions of thinking and acting (Table 1) (Russell & Norvig, 2016). The top definitions are concerned with thought processes and reasoning, while the bottom definitions address the behavior.  The definitions on the left measure success regarding fidelity to human performance, while the definitions on the rights measure against an ideal performance measure called “rationality” (Russell & Norvig, 2016).  The system is “rational” if it does the “right thing” given what it knows.

Table 1:  Some Definitions of Artificial Intelligence, Organized Into Four Categories (Russell & Norvig, 2016).

The study (Patrizio, 2018) defined artificial intelligence as a computational technique allowing machines to perform cognitive functions such as acting or reacting to input, similar to the way humans do.  The traditional computing applications react to data, but the reactions and responses have to be hand-coded.   However, the app cannot react to unexpected results (Patrizio, 2018).  The artificial intelligence systems are continuously in a flux mode changing their behavior to accommodate any changes in the results and modifying their reactions (Patrizio, 2018).  The artificial intelligence-enabled system is designed to analyze and interpret data and address the issues based on those interpretations (Patrizio, 2018).  The computer learns once how to act or react to a particular result and knows in the future to act in the same way using the machine learning algorithms (Patrizio, 2018).  IBM has invested $1 billion in artificial intelligence through the launch of its IBM Watson Group (Power, 2015).  The health care industry is the most significant application of Watson (Power, 2015).

Advanced Level of Big Data Analysis

The fundamental analytics techniques include descriptive analytics allowing breaking down big data into smaller, more useful pieces of information about what has happened, and focusing on the insight gained from the historical data to provide trending information on past or current events (Liang & Kelemen, 2016).  However, the advanced level computational tools focus on predictive analytics, to determine patterns and predict future outcomes and trends through quantifying effects of future decision to advise on possible outcomes (Liang & Kelemen, 2016).  The prescriptive analytic includes functions as a decision support tool exploring a set of possible actions and proposing actions based on descriptive and predictive analysis of complex data.  The advanced level computational techniques include real-time analytics.

Advanced level of data analysis includes various techniques.  The real-time analytics and meta-analysis can be used to integrate multiple data sources (Liang & Kelemen, 2016).  The hierarchical or multi-level model can be used for spatial data, a longitudinal and mixed model for real-time or dynamic temporal data rather than static data (Liang & Kelemen, 2016).  The data mining, pattern recognition can be used for trends, and pattern detection (Liang & Kelemen, 2016).  The natural language processing (NLP) can be used for text mining, machine learning, statistical learning Bayesian learning with auto-extraction of data and variables (Liang & Kelemen, 2016).  The artificial intelligence with automatic ensemble techniques and intelligent agent, and deep learning such as neural network, support vector machine, dynamic state-space model, automatic can be used for automated analysis and information retrieval (Liang & Kelemen, 2016).  The causal inferences and Bayesian approach can be used for probabilistic interpretations (Liang & Kelemen, 2016).  

Big Data Empowers Artificial Intelligence

            The trend of artificial intelligence implementation is increasing.  It is anticipated that 70% of enterprises will implement artificial intelligence (AI) by the end of 2018, which is up from 40% in 2016 and 51% in 2017 (Mills, 2018).  A survey conducted by NewVantage Partners of c-level executive decision-makers found that 97.2% of executives stated that their companies are investing in, building, or launching Big Data and artificial intelligence initiatives (Bean, 2018; Patrizio, 2018).  The same survey has found that 76.5% of the executives feel that the artificial intelligence and Big Data are becoming interconnected closely and the availability of the data is empowering the artificial intelligence and cognitive initiatives within their organizations (Patrizio, 2018).

Artificial intelligence requires data to develop its intelligence, particularly machine learning (Patrizio, 2018).  The data used in artificial intelligence and machine learning is already cleaned, with extraneous, duplicate and unnecessary data already removed, which is regarded to be the first big step when using Big Data and artificial intelligence (Patrizio, 2018).  CERN data center has accumulated over 200 petabytes of filtered data (Kersting & Meyer, 2018). Machine learning and artificial intelligence can take advantages of this filtered data leading to many breakthroughs (Kersting & Meyer, 2018).  An example of these breakthroughs includes genomic and proteomic experiments to enable personalized medicine (Kersting & Meyer, 2018).  Another example includes the historical climate data which can be used to understand global warming and to predict weather better (Kersting & Meyer, 2018).  The massive amounts of sensor network readings and hyperspectral images of plants is another example to identify drought conditions and gain insights into plant growth and development (Kersting & Meyer, 2018). 

Multiple technologies such as artificial intelligence, machine learning, and data mining techniques have been used together to extract the maximum value from Big Data (Luo, Wu, Gopukumar, & Zhao, 2016). Artificial intelligence, machine learning, and data mining have been used in healthcare (Luo et al., 2016).  Computational tools such as neural networks, genetic algorithms, support vector machines, case-based reasoning have been used in prediction (Mishra, Dehuri, & Kim, 2016; Qin, 2012) of stock markets and other financial markets (Qin, 2012). 

AI has impacted the business world through social media and the large volume of the collected data from social media (Mills, 2018).  For instance, the personalized content in real time is increasing to enhance the sales opportunities (Mills, 2018).   The artificial intelligence makes use of effective behavioral targeting methodologies (Mills, 2018).  Big Data improves customer services by making it proactive and allows companies to make customer responsive products (Mills, 2018).  The Big Data Analytics (BDA) assist in predicting what is wanted out of a product (Mills, 2018).  BDA has been playing a significant role in fraud preventions using artificial intelligence (Mills, 2018).  Artificial intelligence techniques such as video recognition, natural language processing, speech recognition, machine learning engines, and automation have been used to help businesses protect against these sophisticated fraud schemes (Mills, 2018).   

The healthcare industry has utilized the machine learning to transform the large volume of the medical data into actionable knowledge performing predictive and prescriptive analytics (Palanisamy & Thirunavukarasu, 2017).  The machine learning platform utilizes artificial intelligence to develop sophisticated algorithm processing massive datasets (structured and unstructured) performing advanced analytics (Palanisamy & Thirunavukarasu, 2017).  For a distributed environment, Apache Mahout (2017), which is an open source machine learning library, integrates with Hadoop to facilitate the execution of scalable machine learning algorithms, offering various techniques such as recommendation, classification, and clustering (Palanisamy & Thirunavukarasu, 2017).

Conclusion

Big Data has attracted the attention of various industries including academia, healthcare and even the government. Artificial intelligence has been around for some time.  Big Data offers various advantages to organizations from increasing sales, to reduce costs to health care.  Artificial intelligence also has its advantages, providing real-time analysis reacting to changes continuously.  The use of Big Data has empowered the artificial intelligence.  Various industries such as the healthcare industry are taking advantages of Big Data and artificial intelligence.  Their growing trend is increasingly demonstrating the realization of businesses to the importance of artificial intelligence in the age of Big Data, and the importance of Big Data role in the artificial intelligence domain.

References

Bean, R. (2018). How Big Data and AI Are Driving Business Innovation in 2018. Retrieved from https://sloanreview.mit.edu/article/how-big-data-and-ai-are-driving-business-innovation-in-2018/.

Kersting, K., & Meyer, U. (2018). From Big Data to Big Artificial Intelligence? : Springer.

Liang, Y., & Kelemen, A. (2016). Big Data Science and its Applications in Health and Medical Research: Challenges and Opportunities. Austin Journal of Biometrics & Biostatistics, 7(3).

Luo, J., Wu, M., Gopukumar, D., & Zhao, Y. (2016). Big data application in biomedical research and health care: a literature review. Biomedical informatics insights, 8, BII. S31559.

Mills, T. (2018). Eight Ways Big Data And AI Are Changing The Business World.

Mishra, B. S. P., Dehuri, S., & Kim, E. (2016). Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing (Vol. 17): Springer.

Palanisamy, V., & Thirunavukarasu, R. (2017). Implications of Big Data Analytics in developing Healthcare Frameworks–A review. Journal of King Saud University-Computer and Information Sciences.

Patrizio, A. (2018). Big Data vs. Artificial Intelligence.

Power, B. (2015). Artificial Intelligence Is Almost Ready for Business.

Qin, X. (2012). Making use of the big data: next generation of algorithm trading. Paper presented at the International Conference on Artificial Intelligence and Computational Intelligence.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach: Malaysia; Pearson Education Limited.

Artificial Intelligence without Big Data Analytics is lame, and

Big Data Analytics without Artificial Intelligence is blind.  Dr. Aly, O.