This is what people sometimes say when you start talking about machine language and artificial intelligence. Though it’s true that a computer can perform long, complex mathematical computations, it could never understand a small boy’s love for a cute puppy. All those emotions that humans have, all those irrational beliefs – it might be hard to find a robot who would get it.
As we’ve all seen in the movies, robots have no understanding of human emotions like love. There are many other esoteric concepts that humans display regularly. These feelings are much more a part of our existence than we think. We take them for granted. But would a machine understand why you’ve kept your mother’s old quilts for 40 years?
Perhaps someday our robots will be sophisticated enough to comprehend emotions like hate, love, revenge, forgiveness, and empathy. Today, however, we’re a long way from that happening. This knowledge hasn’t prevented technology experts from moving forward with innovative ideas. It looks like the future will be full of incredible things we can only imagine.
Artificial intelligence and the attorney
The legal field often deals with strong emotions like love, hate, and revenge. The motive for murder is frequently nothing more than simple jealousy. The motive for robbery or embezzlement is most commonly greed. Since the law so often deals with complex human emotions, will it be able to fully utilize machine learning and artificial intelligence?
Though it has represented a significant hurdle for our best and brightest experts, legal professionals are finding ways that it can be done. Almost any attorney would be interested in learning how a particular judge might rule in this case. Information like this is priceless. But so many lawyers today are unfamiliar with the technology and the terminology. For those who are intrigued by the possibilities available now in machine learning and artificial intelligence, it’s important to know the basic jargon.
The technology behind these applications can be overwhelming for anyone without a background in robotics. However, we can learn the basics about each type of technology and take advantage of the awesome opportunities just waiting for those who are willing to take a step into the future.
Artificial Intelligence (AI)
This is a broad term describing a wide range of technologies that can perform complex tasks that were once only performed by humans. This includes self-driving cars, speech recognition, robotics, algorithmic stock trading, medical imagery devices and a few others. The list grows almost daily.
Artificial intelligence is having a deep impact on the legal field, the business community, the medical field, and the financial industry. As new advancements are made, it will become almost essential to understanding AI well enough to know where you can best implement it in your law firm.
In the legal profession, artificial intelligence is already making many of those time-consuming, redundant jobs much easier. AI can be used to analyze legal contracts, review documents, and manage billing tasks. One of the more exciting applications is in the area of data mining. AI is now being used to sort through millions of litigation documents to find the strategic insights that can help you win your case.
Though machines may never be able to replace a well-trained litigator, they can quickly comb through a mountain of data and extract the exact information you will need to get the right verdict. AI applications offer a wide range of possibilities, and yet they can be targeted to accomplish very specific skills. The trick is in learning which tasks are best done by a human being, and which ones should be completed by artificial intelligence.
Many of today’s law firms are using AI-based legal tools for research. They’re also using them to identify drafting errors, predict outcomes, identify litigation trends and much more. As law firms begin to understand the potential behind machine learning, expect them to venture out more into this realm.
Cognitive Computing and Augmented Intelligence
Cognitive computing is often used interchangeably with AI. However, there is a subtle difference between the two. In the field of cognitive computing, developers strive to simulate the human thought process. This technology utilizes data mining, natural language processing, and pattern recognition technology. The goal is to solve problems without human intervention. Using machine learning algorithms, this technology is continuously acquiring new knowledge. The hope is that someday it will be able to anticipate problems before they happen and provide working solutions.
Augmented Intelligence uses numerous AI applications as well. These include natural language programming, robotics, neural networks and virtual reality. While AI is more about replacing thousands of human actions using a computer program, Augmented Intelligence is more about enhancing the human experience. Humans would use virtual reality and robots to help them with complex, large and small tasks.
In the world of artificial intelligence, you might hear terms like “robot lawyers.” In the world of augmented intelligence, advanced technology would enhance a human attorney’s skills. At the end of the day, both technologies are necessary because there will be times when a robot lawyer might actually be a better idea.
For instance, think about the enormous time spent by judges who are dealing with small crimes like petty theft, speeding tickets, drunk and disorderly conduct, etc. Most of these crimes don’t require a judge and jury. They could easily be decided by a computer algorithm that measures the facts of the case against the written law pertaining to the case. This type of robot lawyer could process cases with better accuracy than a human judge and at a much faster rate. This would equal substantial cost savings to the city and law enforcement.
Most people understand the term “analytics” now due to widespread usage of analytics programs that tell you how your website is doing. Today’s advanced analytics programs do more than just analyzing data. Using sophisticated tools, they parse through massive amounts of data and eliminate anything considered irrelevant or redundant. Next, the data is structured so that it can be readily used. Analytics is a field that constantly changes because new data is being entered and sorted each day. This process is building a colossal database of all knowledge.
An analytics program can be configured to work in specific fields like the law, medicine, sales, stock trading or any number of areas. You might profit from knowing how well appliance sales do on certain days of the week. This same technology could also tell you how likely a criminal is to commit additional crimes.
What does the future hold?
As we move forward, it behooves us all to learn about machine learning, augmented intelligence, analytics, and cognitive computing. As with every new advancement in futuristic technology, the more we know, the more readily we’ll be able to adopt the technology and accept it into our lives. There are already much easier and cheaper ways to accomplish most of our everyday redundant tasks.
You can tell your Roomba to “Start Cleaning.” You can ask Alexa where a restaurant is located and how late they’re open. You can help your teenager with a complex math equation. But, you can’t tell Alexa to pick up the kids from school.
Our machines may someday perform all our normal household and many of our work-related tasks. At the moment though, there are certain things that only humans can do. However, humans augmented by artificial intelligence will be able to complete those tasks much faster and with better accuracy.
The three sectors with the biggest spending on cybersecurity are banking, manufacturing, and the central/federal government, accounting for 30% of overall spending (IDC).
Forty-three percent of attacks are aimed at SMBs, but only 14% are prepared to defend themselves (Accenture).
The average cost of a data breach in the United States is $8.64 million, which is the highest in the world, while the most expensive sector for data breach costs is the healthcare industry, with an average of $7.13 million (IBM).
40% of businesses will incorporate the anywhere operations model to accommodate the physical and digital experiences of both customers and employees (Techvera).
The cost of cybercrime is predicted to hit $10.5 trillion by 2025, according to the latest version of the Cisco/Cybersecurity Ventures “2022 Cybersecurity Almanac.”.
The internal team was energized. With the Level 1 work off its plate, the team turned its attention to the work that fueled company growth and gave them job satisfaction.
More than 33 billion records will be stolen by cybercriminals by 2023, an increase of 175% from 2018.
It takes an average of 287 days for security teams to identify and contain a data breach, according to the “Cost of a Data Breach 2021” report released by IBM and Ponemon Institute.
We did a proof of concept that met every requirement that our customer might have. In fact, we saw a substantial improvement.
We did everything that we needed to do, financially speaking. We got our invoices out to customers, we deposited checks, all the things we needed to do to keep our business running, and our customers had no idea about the tragedy. It didn’t impact them at all.
“We believe our success is due to the strength of our team, the breadth of our services, our flexibility in responding to clients, and our focus on strategic support.”