Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the possibilities offered by the alliance between Big Data and artificial intelligence.
The revolution of AI and Big Data
We are at the dawn of a technological revolution of greater magnitude than the internet and mobile communication technologies. In 1965, Gordon Moore, co-founder of Intel, theorized that computing power would be able to double every 18 to 24 months. For the next 50 years, his theory proved to be accurate. The robotics and biotechnology sectors have made incredible progress.
Today, however, technologies such as artificial intelligence and big data are evolving even faster. The respective exponential growths of these two technologies are about to come together, allowing each to grow even faster. Artificial intelligence is no longer simply a film or a book.
Artificial Intelligence: Technology that Inspires
In 1990, a group of scientists began to decode the human genome. A process that would take them no less than 13 years, and would cost them $2.7 billion. This decryption would not have been possible without the help of immense computer power and custom software. Thanks to the fall in computer prices, the researchers were then able to undertake editing the genome using the CRISPR technique. At present, analytical technologies of big data will make it possible to develop medical treatments adapted to each one according to his genetic code.
Autonomous cars have always held a special place in science fiction. Today, reality is catching up with imagination. In 2009, many luxury brands incorporated assisted navigation systems and adaptive data-driven channel change software. More recently, Tesla has used Big Data and Artificial Intelligence to create autopilot functionality.
For their part, Nvidia and Alphabet use artificial intelligence to make real-time detailed maps used by their test vehicles to visualize the world. The commerce industry is also evolving. Product development and marketing are now driven by artificial intelligence and big data. All these fascinating innovations have been made possible by the meeting of computing power, big data and artificial intelligence.
Big Data and Artificial Intelligence are the Next Digital Disruption
The big data and artificial intelligence technologies are both inextricably linked, so that a Big Data Intelligence can speak. AI has become ubiquitous in companies in all industries where decision-making is transformed by intelligent machines. The need for smarter decisions and big data management are the criteria that drive this trend.
The convergence between Big Data and AI seems inevitable as the automation of smart decision-making becomes the next evolution of Big Data. Rising agility, smarter business processes, and higher productivity are the most likely benefits of this convergence.
The evolution of data management did not go smoothly. Much of the data is now stored on a computer, but there is still a lot of information on paper, despite the ability to scan paper information and store it on disks or in databases.
You just have to go to a hospital, an administration, a doctor’s office or any business to realize that a lot of information about customers, vendors, or products is still stored on paper. However, it is impossible to store terabytes of data produced by streaming video, text, and images on paper.
The mere fact of collecting or having access to large sets of data is not enough to produce a result. Most of us are not sufficiently prepared for knowledge extraction and the demand for rapid decision-making required by customers and markets to maintain competitive advantage.
Today, the use of machine learning, expert systems and analytical technologies in combination with Big Data is presented as the natural evolution of these two disciplines. Convergence is inevitable.
The Internet of Things also represents a convergence between Big Data and AI. Without a digital brain intelligent enough to allow humans to use an IoT network capable of processing, distributing and collecting Big Data, it will not be possible to set up such a network.
Even the sensors, chips, network nodes and software that make IoT networks work on the cloud will be related to artificial intelligence. This phenomenon is already in place in the field of Machine to Machine communications.
The capture data to identify trends or patterns in the behavior of customers or employees can be very helpful. However, the extraction of meaning, and its automation, to discover optimal methods of improving productivity or problem solving could be even more useful.
Artificial Intelligence can be used to make meaning, consider better results, and help in faster decision making from immense Big Data Sources. In a world where Big Data is ubiquitous, the extraction of meaning and the monetization of data will be led by artificial intelligence for the future of business and the development of the planet. The convergence between Big Data and artificial intelligence could help overcome challenges such as unemployment, the environment, the economy, security or health.
Automation of decision-making is slowly becoming the norm. Many problems concerning the ethics of artificial intelligence have yet to be solved. Systems capable of learning autonomously, responsible for determining which Big Data should be identified and used, will require human management, at least initially.
In the fields of healthcare, law, banking, advertising, fair trade, security or finance, big data alone is not enough. It is necessary to use artificial intelligence in addition.
It is therefore important not to make the mistake of perceiving these two technologies as two separate tendencies. Your business might miss an opportunity. This convergence will have a direct impact on your employees, your customers, your services and your market and must be taken into account.
What are the challenges for Big Data and Artificial Intelligence?
For the moment, artificial intelligence is not regulated in a specific way. Many people express security concerns. This problem needs to be resolved quickly. Any information can be easily stolen by hackers. Highly sophisticated models make us vulnerable to many threats.
Moreover, many worry about the control around this technology. The lack of laws to govern the sales and purchase of AI software. If these programs are intended to control traffic, health systems, or the stock market, it is necessary to put in place governance laws.
There is no doubt that autonomous decision-making is the future. However, again, many fears are emerging about the authenticity and ethics of artificial intelligence and Big Data. The accumulation of data on cloud servers and its accessibility to fraudsters can be fatal for businesses.
All these challenges are daunting. They give rise to suspicion around this convergence between AI and Big Data. It is important to remember that technologies are only disruptive when we are poorly prepared.
Can Big Data solve the problems and dangers of Artificial Intelligence?
Over the last four years, agreements between large companies and startups dedicated to artificial intelligence have increased significantly. This number rose from 160 in 2012 to 658 in 2016. Companies use artificial intelligence for a wide variety of uses, ranging from autonomous car development to remote sensing of emotion.
Apart from these uses, artificial intelligence can be even more useful for businesses through what is called Account-Based Intelligence.
Account-Based Intelligence is the latest iteration of the dream of sales and one-to-one marketing. Today, we are closer than ever to achieving this utopia.
Example of Artificial Intelligence feeding on Big Data
First, we are generating more data today than ever before. Every second, humanity produces 6000 tweets, 40,000 Google searches, and 2 million emails. By 2019, global web traffic will surpass 2 zettabytes per year.
This huge amount of data is the first step towards Account-Based Intelligence, because the ABI requires granular information about each target company. However, it also raises a new problem. Companies must find how to turn this data into exploitable insights.
Indeed, this task is impossible to accomplish using traditional marketing tools or simple Google searches. The web is too massive is disorganized to achieve it as well. Many companies spend millions of dollars to mix data sources and solution points, which ultimately results in only a very low conversion rate. For good reason, this method usually results in sending the wrong message to the wrong people at the wrong time.
Artificial Intelligence tools for the ABI
Until recently, computers struggled to interpret unstructured data like Facebook content and YouTube videos. However, with recent advances in cognitive computing and processing power, things are changing.
However, this change can benefit businesses for their sales and marketing. Indeed, information on business leaders, the decisions they make, their attitude and demographics are not stored properly in small databases. They are scattered in social media publications, browsing history and geolocation data. Today, new tools allow startup leaders to make sense of this data.
The Data Web Crawlers undermine autonomously in search of unstructured data. They examine entities, establish relationships, and create customer profiles. With an estimated 70 percent increase in data per year, it is critical that these programs continually scan the web for the most relevant information.
Startups can use them to deploy the ABI. For example, to find new customers, browsing the web can reveal a niche of customers whose demographics match those of the best current customers.
In 2015, Microsoft acquired Metanaunix for this purpose. By using crawlers, the startup can explore a large amount of non-relational data. It then recovers insights from different sources faster and more accurately than humans.
Natural language processing
The natural language processing can examine the interactions between computers and humans to extract meaning from conversations. By spotting some words or phrases, this technology helps to analyze feelings about the brand. It also predicts which audiences will be more receptive to the company’s message. This is essential in order to communicate the right message to the right people, which is the primary criterion of the ABI.
If the company wants to know what people are saying about its products on social networks, natural language processing can explore social media publications, link them with certain consumer groups, and find out what’s important the most for each group. This system can be used to respond to consumer criticism and positive reviews, to solve problems, and to improve a product.
If you want to try this technology for yourself, be aware that the IV.AI startup allows anyone to try out their natural language processing platform. Type any phase to know the emotion that corresponds to it.
The Machine Learning allows computers to learn and act without being programmed explicitly. This technology looks for patterns within the data to direct the actions of the programs, taking into account the context. The true ABI requires dynamic templates, and the Learning machine automatically adjusts them as new data emerges.
Without even knowing it, new companies are already taking advantage of Machine Learning. Facebook uses this technology to personalize the news feed based on clicks and likes. Other companies use this technology to predict customer loyalty or purchasing behavior, predict product performance, or anticipate risks.
Google Now is probably the most advanced Machine Learning app yet. She learns user habits, mimics their conversation style, and provides them with smart recommendations. For example, if the user needs to go to the airport for a flight that will take place in 30 minutes, Google Now can analyze the traffic delays and schedule an Uber that will take him there on time.
Artificial intelligence is strong and without a doubt, a great technology. It can find data inaccessible to humans and distill meaning with great precision. Combined with the ABI, it can also guide the company to its next best customers. This technology will be the biggest change of the century in the field of business, and the revolution is just beginning.
Big data trends that will lead the evolution of AI
The rise of AI and Machine Learning is highly dependent on Big Data. The data make it possible to develop predictive models. The more data that exist that are representative of the concepts to be learned, the more Machine Learning AI applications are completed.
We should see more experts in this area, but demand should remain above supply. Machine Learning promotes the adoption of Big Data solutions, just like the cloud that facilitates their deployment.
Big Data self-service tools available on the web
With advances in data processing applications, there are many free online Big Data platforms available. These cloud platforms make it easy to organize and synthesize data, even for beginners.
It is enough for the user to specify the amount of storage and computing power it needs, and the databases appear in the cloud in minutes. No need to configure racks, networks or servers.
For Michael Cavaretta, Director of Analytical Infrastructure at Ford Motor Company, this trend is expected to continue. Big Data’s cloud implementations are becoming increasingly popular as they reduce the cost of accessing these technologies. For many, developing a Big Data stack is not cost-effective, and works best when most data can be hosted on an individual instance.
Analytical technologies are struggling to adapt
Even with state-of-the-art tools and data warehouses such as Hadoop and Spark, data analysis remains complex. Companies struggle to transfer their data from operational systems to analytical systems. This difficulty directly affects productivity.
The data available is no longer numerous, and the algorithms are improving, allowing more automation and better predictions. In fact, analytical technologies are struggling to adapt.
Data cleaning becomes an industry
To transfer data to Machine Learning systems, it is necessary to clean them first. Cleaning up data means looking for errors in the format or duplications within the database. The quality of Machine Learning systems depends on the data on which they are based. The secret is to turn raw data into actionable data. For example, knowing that someone has visited an online shoe store is helpful, but knowing when he or she visited is an invaluable piece of information.
The democratization of data
The data director of the Toyota Research Institute believes that data does not reside in data lakes but in silos in which their mission is clear. Server-less and micro-service architectures make it much easier for owners of these silos to access, analyze, and manage their data without having to rack up servers, configure virtual machines, or even to the payment by the hour. Data owners can therefore focus on data enforcement and pay for what they use by the minute.
Artificial intelligence is a sector with great potential to transform the fields of science, medicine, and technology. We can only advise companies to be ready to embrace this new technology.
The big data and artificial intelligence are emerging technologies, and it is impossible to predict their effect on the long term. It would be absurd, however, to ignore these technical advances. These two technologies are likely to converge in the very near future.