Digital Transformation — Path to Intelligent Organization.
Do Machines learn? Yes! Machines learn by studying data to detect patterns or by applying general rules to Categorize or catalog like people or things, Predict likely outcomes or actions based on identified patterns, Identify hitherto unknown patterns and relationships and Detect abnormal or unexpected behaviors.
The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment is provided to the “machine,” the algorithm’s performance improves, increasing “intelligence” over time.
What is Digital transformation? It arises from the intersection of cloud computing, big data, IoT, AI, Blockchain technology. I describe — it is the integration of digital technology into all business areas, fundamentally changing how you operate and deliver intelligent value. Others refer to digital transformation as the “Second Machine Age” or “the fourth Industrial Revolution” — MIT professors Erik Brynjolfsson and Andrew McAfee argue that the crux of this machine age is that computers are now able to learn and not just follow instructions. Computers diagnose diseases, drive cars, speak to us — the list goes on and on. The first Industrial Revolution allowed humans to master mechanical power. In the last one, we harnessed electronic power. In the era of digital transformation, we will master mental <Intelligence> power.
What technologies do we typically associate with Digital Transformation? · AI - Artificial Intelligence
· ML - Machine Learning & DL - Deep Learning
· RPA – Robotic Process Automation
· Predictive Analytics
· Blockchain
First, let me talk about AI itself — The third major technology driving digital transformation. Artificial intelligence — code that learns — is likely to be humankind’s most important invention. In recent years, the exponential growth of data made computers “hungry” to analyze big data. AI is the science and engineering of making intelligent machines and computer programs capable of learning and problem-solving in ways that generally require human intelligence. Artificial Intelligence applies machine learning, deep learning, and other techniques to solve actual problems.
The timeline of AI art shows us how AI can collaborate with humans and create something different and extraordinary; AI can co-produce, co-design, co-paint, co-act.
Taryn Southern is a pop artist working with several AI platforms to co-produce her album I AM AI. Her 2017 single “Break Free” below is a human-AI collaboration. You can hear about Taryn’s creative process in How AI-Generated Music Is Changing The Way Hits Are Made, an interview with DJ and Future of Music producer Dani Deahl. Taryn explains: “Using AI, I’m writing my lyrics and vocal melodies to the music and using that as a source of inspiration. But, because I’m able to iterate with the music and give it feedback and parameters and edit as many times as I need, it still feels like it’s mine.”
What are Machine Learning and Deep Learning? Deep learning represents the next evolution of machine learning. In machine learning, algorithms created by human programmers are responsible for parsing and learning from the data. They make decisions based on what they know from the data. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. Deep learning machines don’t require a human programmer to tell them what to do with the data. It is made possible by the extraordinary amount of data we collect and consume — data is the fuel for deep-learning models. In reality, AI is a part of ML, which includes many different methods (regressions, decision trees, ensemble methods, clustering, Deep Learning, etc.)
Can a machine generate the next Picasso masterpiece on its own? This question was thrust into the limelight by artist collective Obvious, a Paris-based trio fascinated by the artistic potential of artificial intelligence. First, fed an algorithm 15,000 images of portraits from different periods. Next, the algorithm generated its illustrations, attempting to create original works that could pass as artificial. When it went under the hammer in the Prints & Multiples sale at Christie’s in October 2018, Portrait of Edmond Belamy sold for an incredible $432,500, signaling the arrival of AI art on the world auction stage.
What is Robotic Process Automation (RPA)? — A business process automation technology based on software robots or artificial intelligence; Think of it as a “robot” to emulate the actions of a human interacting within digital systems to execute a business process. RPA robots utilize the user interface to capture data and manipulate applications just like humans do. In addition, they interpret, trigger responses, and communicate with other systems to perform a wide variety of repetitive tasks. Only substantially better: an RPA software robot never sleeps and makes zero mistakes.
What is Predictive Analytics? The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.
What is Blockchain? A decentralized, distributed ledger of transactions with elements of transparency, trust, verifiability, and something called smart contracts. Decentralized and distributed means that information is stored across the network so that each endpoint has access to the data without requiring access to a central server. The network is also distributed because the transactions happen at each endpoint without requiring centralized coordination. A ledger is a record of transactions. For example, blockchain records a ledger of interactions between two different parties, whether a financial exchange or even a chain of custody, showing when things have changed hands over time.
Those companies which do digital transformation will survive, change and scale — It’s not just about process automation anymore; ML, RPA, and AI have the potential to revolutionize nonprofit finance. Learn more about how AI is used to continuously monitor performance and security, detect anomalies or irregular transactions in real-time, and eliminate the close. In addition, discover how AI is helping accelerate time to actionable insights and speed time to revenue.
The need to train people in new digital technologies — With change comes uncertainly. Employee roles will alter. Many will worry about redundancy and job changes. Companies have a responsibility to put people first and be responsible for upskilling them to adapt to new roles. Mastering new skills, such as data analysis, AI, and ML, will prove any workforce from the potential downside of change. Those who embrace change will have the advantage of a robust business infrastructure based around cloud technology. With that comes the ability to scale up or down according to the business needs. For example, the finance function will live up to its evolving responsibility, harnessing data and analytics to keep an organization on top of every mission-critical decision it needs to make.
Author/Writer — Ketevan (Kate) Koraia
