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The Future of AI in the Practice of Law
In this episode of The Kennedy-Mighell Report, hosts Dennis Kennedy and Tom Mighell consider the increased popularity of artificial intelligence, the usefulness of chatbots, and how both innovations can impact the practice of law. Tom speculates that the current data age and the large volumes of information available for analysis have helped to enable the advancements in machine learning and artificial intelligence. Dennis explores exactly what machine learning means and explains the three current learning types: unsupervised, supervised, and reinforced. Tom finds the technology perplexing and uses the definition of Tenser Flow to illustrate how grasping these advanced concepts requires more education and technology knowledge than the average lawyer possesses. They both discuss the AI lawyer Ross and if legal professionals should gain technical knowledge in order to influence future ethical regulations with emergent technology. They end the first segment with a list of possible ways that these advancements in tech can aid lawyers in their everyday lives.
In the second segment of the podcast, Dennis and Tom talk about chatbots and how they can help lawyers with their daily tasks. Dennis proposes that they get a chatbot for the show and Tom strongly disagrees. Tom emphasizes that there are two ways to create a chatbot: programming one manually or allowing one to learn via data analysis and that he is fine observing what innovations programmers create. They both discuss how chatbots can help lawyers automate their scheduling needs and how utilizing this technology can save law practitioners valuable time. As always, stay tuned for Parting Shots, that one tip, website, or observation you can use the second the podcast ends.
Special thanks to our sponsor, ServeNow.View transcript
The Kennedy-Mighell Report
The Future of AI in the Practice of Law
Intro: Web 2.0, Innovation, Trend, Collaboration, Software, Metadata…
Got the world turning as fast as it can, here how technology can help, legally speaking, with two of the top legal technology experts, authors and lawyers, Dennis Kennedy and Tom Mighell. Welcome to the Kennedy-Mighell Report here on the Legal Talk Network.
Dennis Kennedy: And welcome to episode 172 of the Kennedy-Mighell Report. I’m Dennis Kennedy in St. Louis.
Tom Mighell: And I’m Tom Mighell in Dallas.
Dennis Kennedy: In our last episode, we looked at recent developments and trends in mobile apps and although we’ve talked about Artificial Intelligence in earlier episodes. There’s been a crescendo of discussion lately about AI and whether AI can even replace lawyers.
Every now and then we’d like to do something we call joining the conversation, and so what we are going to do in this segment today where we boldly dive into the world of AI and machine learning and wonder if everybody is focused on the right questions.
Tom, what’s on our agenda for this episode?
Tom Mighell: Well, Dennis, in this edition of the Kennedy-Mighell Report, we’ll be discussing Artificial Intelligence and machine learning, how these developments might affect lawyers.
In our second segment, we’re going to ask whether it’s time to get our own personal chat box speaking of Artificial Intelligence, and as usual we’ll finish up with our parting shots, that one tip website or observation that you can start to use the second that this podcast is over.
But first we want to talk about AI (Artificial Intelligence), why it’s suddenly red hot right now and because it’s a legal technology podcast whether AI is going to replace lawyers anytime soon because that seems to be the question that gets asked any time there’s an advanced technology that comes out.
Dennis, people have been talking about how we’re on the verge of huge Artificial Intelligence breakthroughs for many years and we never quite seem to get it, but I have to say with recent advances in technology I think we might really be on the verge of something.
I will say that I have not been following this topic as much as you have so I’m going to be following your lead to a certain extent during this podcast, but let’s start out, and Dennis, tell us why is this a hot topic especially for lawyers right now?
Dennis Kennedy: Well, it’s everywhere I turn I see something, so from blogs to twitter to some of the legal publications, I saw something today where people were considering whether there is to be ethics opinions issued about lawyers using Artificial Intelligence.
It just seems like everywhere I look I’m seeing some conversation and it’s the most drastic of the questions, which is, will Artificial Intelligence take the place of lawyers and then there’s usually some conversation where people with a sigh of relief determine that, no, Artificial Intelligence right now is not able to take the place of lawyers and kind of like, well yeah.
So I had this conversation with somebody the other day and I was talking about Artificial Intelligence and because some of the discussion lately asked the common questions and some people say Artificial Intelligence work goes back, I don’t know, 40-50 years, but when I was in law school in the early 80s I had a class on computers and the law, one of the first seminars of that type in the country and we spent a couple classes where we were talking about Artificial Intelligence.
And we felt that was like fairly close that we talked about a lot of the same questions that people will talk about these days, and then probably every, I don’t know, 5-10 years I think we’d get a burst of this and it always feels that Artificial Intelligence is just around the corner but we never quite get there and I think there is a number of reasons for that we’ll jump into.
But I think that’s why it’s become hot these days is that some new technology developments, big data, machine learning, lots of things going on in the cloud and just this whole smartness of different apps and devices around, Siri, those sorts of things where people start to say, well, there is IBM Watson and all of a sudden people are kind of like, oh wait, maybe we are on the verge of AI.
So there might be something there. I’m curious, Tom, why you intro this by taking that maybe we actually are on the verge of something?
Tom Mighell: Also, I think that the main reason why I think we’re on the verge of something is, I think it has something to do and I hesitate to wade into the water here because I know there is a lot more people who know so much more about this than I do, but I think that part of what makes Artificial Intelligence or machine learning possible is huge amounts of data and lots of information.
And I think that as we get more into the “big data era” as big data becomes more the norm and we have large volumes of information to analyze, that’s the best case scenario for machine learning tools because they do best when they have a lot of information to understand. That’s how machine learning tools get smart, is they analyze a lot of material and they start to see patterns.
And computers can see patterns of visualizing things, so if they want to see a cat they can see a cat in a picture and a computer can pick that thing out, and what I think is really interesting is that we’ve always comforted ourselves by saying that the machines will never be smarter than us, but one way that they are starting to become smart like us and I’ll talk a little bit later about why I think that they’re not quite all the way there in a minute is because that they are really getting good about understanding the context of what you’re trying to get to, that they are not just answering a linear question and only answering that linear question, but they actually are seeing things around it and are providing more useful information based on what they believe to be the context of what you’re asking. And my favorite example is still to a certain extent, Google search when you ask it a question, who is the 40th President of the United States? It gives you that answer, Ronald Reagan.
And then the next question you ask is, where did he go to college? And it understands the context of what you’re asking for and will immediately tell you where Ronald Reagan went to college, and I think that what’s making that possible is the ability to analyze lots of information quickly and spot those patterns, I think that that’s something that has improved vastly over probably the last time we got excited about AI.
Dennis Kennedy: Well, I think it’s partly that, I’m going to go the opposite direction on the big datasets in a second here, but I think I see three things. So the training that we have been able to do on the big datasets and what we’ve learned from that, and then this whole notion of machine learning which is basically coming up with software that is able to learn.
So you set it loose on data, and what’s interesting is it can be some smaller datasets as well, but basically it’s the rules in there that allow the learning to happen, and so we look at the things that are now on our cars, the Siri, the Google things that you talked about, all those things, I think there is this notion that the machine learning tools have really started to make that happen.
So in a way when you think about classic AI, this is the flip of it, because in the beginning people were saying we need to design something that works exactly like the human brain and we need to figure all the structures of what knowledge is and all those sorts of things.
Now it’s sort of like we have these tools we could turn on data and I think in simplest terms it helps us recognize patterns and then do something with that, and then there is the notion of neural networks which I always kind of struggle with because it’s not anything I’ve ever worked with, but the idea that you do have this parallel processing that is designed to mimic thought, but certainly to do things faster and in parallel. So I think all those things combine.
The thing I am going to recommend to people though is that I listen to podcast called Hanselminutes. The July 7 episode, a guest named Andy Kitchen, he did this great intro to machine learning, AI and neural network, so it’s as good as anything I’ve ever heard. So I definitely want to send people to that, and Tom, that will go into show notes I know, but what was cool that they were talking about is an open source tool that’s available called TensorFlow, that would allow you to do some simple experiments.
And it could be on pretty small datasets, spreadsheets or other small data bases you have where you can start to play with the machine learning tools and see what might happen so that general principle, those general tools and then the ability to go into specialized AI has really started to open things up.
Tom Mighell: Well, I have to say, and this is where I become the grumpy old man who refuses to learn things, but I have, you recommended, you said, before we do this, go and listen to the Hanselminutes podcast, go and look at TensorFlow; I did that and I just have to tell you, I am completely mystified by the whole subject because I just read a description of TensorFlow that I got from the website.
“TensorFlow is an open source software library”; got that, I understand that, “for numerical computation using data flow graphs”; okay, still with me mostly but I am a Liberal Arts major, so that’s why this next part, “Nodes in the graph represent mathematical operations while the graph edges represent the multidimensional data arrays communicated between them”, and I have just passed into a land that I know none of this.
And so I think what I take from this is, is that, yes, I think that there are some folks out there who might want to experiment with this stuff, but this is not easy stuff. This is not stuff that I think is something that anybody can just go and pick up and do, and so, I’m going to be the cranky get off my lawn kind of guy here because I think that some of — getting more interested in this is going to take more education and knowledge than the average lawyer who may be willing to devote to this whole issue. And maybe the question is, should we start to get that knowledge and is there a value to us learning more about it or just appreciating what it can do for us?
Dennis Kennedy: Well, I actually think both. When I was reading the stories about whether there needs to be ethical regulations of lawyers using Artificial Intelligence and if we all take the approach of like, hey, this is too complicated for us then people are going to slap rules on things that have no relationship with reality, and we know from even the error of the webpages and stuff that once you start putting these ethics rules on keeping copies of websites that are generated by databases on-the-fly stuff like that, you could disconnect from reality.
So I think having a basic understanding and then having the ability to translate from the technical to more plain language world it is important, but not for everybody. I think that most of us are interested in the apps that come out and the tools and how that can help us. And so, I just want to highlight the two ones I think that the people become familiar about in the last year or so.
So one is the IBM Watson which became famous for winning jeopardy but the applications that are coming out of that is that tool gets put on to different datasets and then the big one in the AI world was the Chinese game of Go, the great Go game masters were defeated by an Artificial Intelligence program designed to play that game and during the game one of the masters felt that the software made some really innovative moves that made sense within the game that the master hadn’t seen before because Go was the next step after Chess in Artificial Intelligence.
So those two things have happened and that make people sit up and take a look and say, hey, is the Artificial Intelligence thing both closer and can it possibly happen faster than what we expected and is the notion of what we think Artificial Intelligence is changing.
Tom Mighell: And frankly what I think is interesting is, like you say, I think the application of the technology to specific things is what’s really more intriguing to me, and you mentioned Watson.
Watson has a little brother named Ross and Ross is marketing itself as highly intelligent legal research where you can ask a question, is it legal for individuals to own otters in the State of Oregon and within seconds it’s going to bring you back unlike Lexis or Westlaw that might bring you back tens of thousands of results which we used to think, hey, that’s great, because there is a lot of stuff to go through, it will bring you back some highly targeted results. It’s going to alert you to changes, it’s going to learn from your queries, but the one thing it can’t do is it can’t make judgments. What I find intriguing are that there are tools out there that can help lawyers answer that question, is it legal? Can I find this research?
We talked about predictive coding here for a long time. This is where I think your example of using a small dataset to train something is true. With e-discovery you can train predictive coding with a relatively small dataset compared to some of these other tools but you can say which of these two million documents is relevant to this particular issue or this particular person who we’re looking for information on. That technology is available.
The technology is available to say how often does Judge Smith rule against the plaintiff in patent cases? That technology is available also. Do any expense items on this bill seem unreasonable? You can train machines to look for those types of things, and those are actually all tools that exist now. They are all things that are out there right now and I think that’s really, really very interesting.
What I was going to talk about was what I would see as the limitation of AI is that AI cannot replace creativity. That would be my argument anyway that taking a client’s problem and coming up with unique creative solutions that’s tailored to that client and their specific fact pattern is something that AI would have more trouble with.
But then you bring up the example of Go and the Go Master talking about innovative stuff and I’ve seen a definition of creativity being the unpredictable combination of ideas coming together at the right moment. Well, there is no reason why a computer can’t do that and just keep putting together random ideas and coming up with something creative, even though it may not know at that moment in time that it’s creative.
So I’m really intrigued by that possibility but I’m not sure we’re quite there yet at least in the legal field.
Dennis Kennedy: Well, I want to go back and talk a little bit about machine learning to give us a little more background and then I think we’ll dive into the big questions big time. So in this world they say there are three types of machine learning and I think this is really helpful, it was very helpful to my understanding, which would be unsupervised learning, supervised learning, and what they call, reinforced learning.
So simple ways to think about this, so I can have a machine learning tool that I turn loose on some data and I let it through its algorithms, and the way it’s set up, it learns the patterns on its own and whatever results it comes up with could be interesting, could not be interesting, could be right, could be no more or less right, but we will gradually learn without any interference and so we’re just interested in the patterns that it finds.
Supervised learning is more where we say we’re doing something and we know what the answer is and we want to improve the learning so it learns what that answer is and then it can repeat it. So in the legal world I think that’s like training the new associate. So you just say, okay, go out there and here’s what I want you to get and I want you to learn how to do it, and I keep doing that until you get to the point and then I turn you loose on other things because you’ll find the same things that I want you to find.
And then the third thing is this reinforced learning, which is interesting to think in terms of software, but the idea is to say, you set it loose and then as it comes up with right answers you reinforce that, you reward it in a sense but you tweak it in recognition that it’s doing a better job of learning so it’s like when we give people incentives to learn things.
And so, to me that whole approach becomes interesting, and that’s why I think what’s going on now is we flipped over the notion of what traditional AI was. So that’s out there.
So Tom, I want to dig into the big questions which are when you talk about I think that humans are creative, I think that shows that we’re trying to say, well, Artificial Intelligence has to be something that takes the place of humans, and that’s what I struggle with, because there is a sort of moving the goalpost notion of AI over the years so it used to be like we would have Artificial Intelligence once if you are communicating that sort of touring test. I’m communicating with a computer and I don’t realize its software that I’m communicating with, and so once that happens, you go like, well, that doesn’t really count and then it became like, well, Chess is the real test.
So if a computer can beat a human in Chess then we have Artificial Intelligence and then that happens and they go, well, actually they need to beat them two out of three, so then we need to be like a really good Chess player, and then it needs to be the Chess Master and then it’s like, well, Chess isn’t a good test, it should be Go and so the goalpost is always moving. And so that’s why I think like, will Artificial Intelligence replace lawyers? We go through the same thing where you go like, oh, it can do this, we go, oh, that’s not really what lawyers do, that’s not practicing law. It can’t replace — it can replace one lawyer, but it can’t replace a really good lawyer.
And so, I think that my feeling is, it always takes us in the wrong direction, and so I look at AI as a toolset and what has the real potential and what’s happening in a world of Chess is that there is a combination of humans using AI that is winning all the tournaments.
So that’s where I get to and I reach this conclusion where I start to think AI is to mean less Artificial Intelligence, it’s something I will call Assistive Intelligence, so it helps us do things better.
So that’s a long answer I know, Tom, but your thoughts on that?
Tom Mighell: Well, no, I think that’s right, and frankly, I think that AI is no different than any of the other technologies to talk about on the podcast. They help lawyers do things faster and more thoroughly and more efficiently than they can do it now.
All of those use cases that I brought up earlier, doing research in a specific area, finding out how a judge rules or how long does it take for a case to get to trial in front of a judge, those are things that we’d all like to know the answers to, but to actually go out and do them will take tremendous amounts of time, let’s turn a machine to that task to get a set information, it doesn’t replace the lawyer. It doesn’t wind up making the lawyer obsolete but it definitely provides that assistance to the lawyer to be able to do their job better.
Now, that said, I know you put in the show notes about the parking ticket chatbot, which to a certain extent is some level of artificial intelligence. There is a bot in the UK for HYPERLINK “http://www.donotpay.co.uk” donotpay.co.uk that you can talk to if you receive a traffic ticket and now they’re allowing you to contest or receive compensation for bad airline treatment, and I cannot remember what the statistic was, it got 160,000 tickets overturned.
So to a certain extent that level of artificial intelligence is replacing lawyers, but I think that that may be an area in which the self-help industry is serving a market where legal help may be on the way out and that it’s not as needed or there is more of a need for maybe supplementation. It’s a different kind of assistance, but nevertheless assistance that this type of AI is providing. I don’t know what you think about that.
Dennis Kennedy: No, I think that’s good. I think that it can and it’s, in sort of what I see the bad form of that, people say it’s the moving the goalpost thing, where you say, okay, well parking tickets, that’s not something lawyers really do so that’s not like a good test, so I see some of that. But what do clients or in some cases what do the people who don’t know whether they should be clients or they can’t afford lawyers, how is this going to help them and you can kind of see things that will work there.
Then also, I think that the legal tech startups and people looking at legal as a space to get into, the legal datasets are really cool. You start to think, what could you do if you kind of turned machine learning on it and then developed AI in relationship to it?
So what if you have the whole patent database, you had big sets of case law, you had law firm client data, what could you do as a practitioner if, let’s say, you are a patent lawyer and this AI tool will say, hey, the invention that you are looking at is like these other things? Because right now the idea of doing that as a human on your own is beyond challenging and the idea that you could find patterns and you could see this in discovery and elsewhere that you wouldn’t expect could make you really so much better as a lawyer.
Whenever I talk about AI I go back to the story that my friend Michael Kraft told me and he was talking about a tool called DolphinSearch, which was one of the early e-discovery tools, a really interesting tool, and Michael said to me think about a basketball and think about the properties of that basketball and just kind of think of the main ones you think of, and you go, well, it’s round, it’s orange, you use it when you play a game.
And then he said, now imagine that you are on a ship that’s sinking, now what do you think of the properties of a basketball, and you go, oh my God, it floats. I can use it as a lifesaver. And so the fact that those AI tools could pick up those patterns and those relationships could be a huge help.
So I like that — I know we need to wrap up time here, but I think it’s a fascinating topic. I think it’s especially interesting because so much does seem to be happening now, and also like I said, I think that the existence of those big legal datasets and sort of the complacency lawyers have about protecting, raising up the drawbridge around the areas that they want to try to keep makes the legal area ripe for experimentation with AI and potential competition and that the parking ticket chatbot may just be the first of those.
Tom Mighell: Well, I certainly think that that is a good place to leave this, so good topic and let’s move on to our next segment and before that let’s take a quick break for a message from our sponsor.
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Tom Mighell: And now let’s get back to The Kennedy-Mighell Report. I am Tom Mighell.
Dennis Kennedy: And I am Dennis Kennedy. We talked about bots in the earlier segment and we talked about bots in a recent episode and I have been noticing chatbots everywhere since we started talking about it. When you go on to a car dealer site and boom, there is somebody saying, hey, I am available to chat with you about the car if you are interested, and that’s clearly a chatbot. It’s automated, and it may get you to a human at some point, but it may not need it. And in some cases in customer support that chatbot might be all you really need.
So I have been listening to some tech podcasts where people have talked about how they have their own chatbots. So my initial reaction of course is, and you know me well enough Tom, that that means I feel like I have got to have my own chatbot now. I am not really sure what that would be. I understand how other people are using chatbots, but Tom, once again reality check time and that’s why I come to you, should I or we have our own chatbot and maybe should this podcast have its own chatbot?
Tom Mighell: No, and this is going to go down as my grumpiest podcast ever I think, but frankly, the ideas and the reasoning is kind of the same. My main observation here is that I am guessing that the people who have their own chatbots are not lawyers, they are probably tech people.
My second observation is you really must want to use TensorFlow a lot, because really to my knowledge the only way to create a chatbot is to learn how to code and frankly there’s two ways to create a chatbot these days. One is to do it manually, hard code it, where you build in the questions and the answers and all of that, that’s hard, that’s a hard thing to do. And the other one is really what we talked in our first segment is to point a lot of massive data at it, let it learn from that information, help those chatbots learn and help them work.
And so I guess my reality here is, I don’t need my own chatbot. I am comfortable seeing what others are doing and I am looking forward to see what others are doing. I like having conversations with chatbots to see what they will say, how they will react to things, the services that they can provide to me no matter whether it’s making reservation some place or whether it’s buying tickets or just getting information, I am really intrigued by that.
So I am going to just say that as far as lawyers are concerned my advice is, sit back, enjoy and understand how chatbots work, but at least for most of you, don’t spend the time learning how to code to do that, and there’s my grumpy statement.
Dennis Kennedy: Well, interesting that you are so grumpy since you are the one who puts the chatbots into our Slack stream.
Tom Mighell: I don’t code them, they have already coded themselves. I love working with chatbots; I just have no desire to create my own.
Dennis Kennedy: And I think you are right on that, but I have faith in the power of cloud and the idea that these applications, especially for simple chatbots are just going to become routinely available, in the same way that it became really easy to do any number of things once they got up into the cloud. There is an interface to do that.
And it’s possible some of our listeners will go, jeez, Tom and Dennis, you missed the obvious things, but I think that it’s one of those things where I sort of feel like it would be just fun to try as a new technology, but we were talking earlier, it’s kind of hard to figure out exactly how I would use it. Because the people who are using it sometimes they will have like a — they do services or they have a product or something and that chatbot will kind of be the entry point to responding to somebody who is a customer and kind of lead them to the right place, where they can order something or do something like that.
And so I think that it’s one thing where you could say, could I do something simple, so a chatbots, that is a way to do simple client intake, a chatbot that allows somebody to set an appointment, a chatbot that says, oh, here is how to get our article on this topic or our checklist for estate planning, and it just becomes this sort of quasi responsive bot that works as an interface of a type, so people aren’t clicking, they are interacting with this bot, that gives them the illusion of something human. And then maybe messages somebody if there is a question that needs follow-up on so that the human then can come into the conversation.
So some of the people I heard talking about this actually do something like that, where they will be pinged that there is sort of like live person with a question they might want to answer, and I think you can — obviously with programming, if you are good enough you can figure out all kinds of things to trigger those sorts of alerts. But it’s funny Tom, because usually we are playing around with new technology and looking at things, and it’s sort of rare that you find something where you go like, oh, this appeals to me personally, like Alexa or some of those other things, but having the chatbot that actually responds to people for me is just one of these technologies.
And I guess it’s just one of my little things that I think could be interesting and maybe watch my website over the rest of the year you might see a little bot appear, if somebody can make it easy enough for me to just grab a code that I can plug into my website, I would love to try it and just see what happens.
Tom Mighell: Well, and for those of you out there who are intrigued by this, but maybe not in the way that you want to create your own bot, I recommend taking a look at Amy, I think it’s Amy.ai. is an artificial intelligence tool that I know that some members of the Legal Talk Network use to schedule meetings. And all you have to do is when you send an email to someone, you copy that Amy’s email address and say, Amy, can you please set up a meeting for me with Tom, and Amy will email you back and say — they will take a look at your schedule on your calendar and say that so-and-so is available at this point in time, can you make a call at that point, and they will go ahead and schedule it for you.
And it’s fairly limited in what it can do, but at the same time it’s automating the process of scheduling those meetings and it’s a really intriguing use of the chatbot feature, even though it’s through email and not through text messaging or other way. So it’s out there and it’s available to look at, and I agree, it’s very interesting and I can’t wait to see where it goes.
Dennis Kennedy: And whatever is left that we humans do uniquely, at least these tools give us more time to figure out what that is and spend more time on it. So now it’s time for our parting shot, one tip, website, or observation you can use the second this podcast ends, Tom, take it away.
Tom Mighell: So I have two parting shots. I received a message on Twitter that any lawyer that talks about what Pokémon GO can do for lawyers would be dead to them, and so I accept this challenge. I want to say three things about the new game from Nintendo call Pokémon GO. If you haven’t heard about it, you must be under a rock somewhere. I want to say three things about it.
One, it’s a great example of augmented reality, a cool way to experience how augmented reality can augment your world and put things on top of what you are actually seeing. So a cool use of that technology.
Number two, in just a few days since the game came out we have already seen the possibilities for legal representation, criminals using the game to rob people, or people who are falling and injuring themselves while walking in public, looking for the items in the game. I think that’s intriguing for lawyers.
And then really just the third observation is the company’s value increased $7.5 billion in two days. That is a technology that is hard to ignore. Anyway, that’s my fun observation.
As far as my other tip, you may have seen the news lately that Evernote has announced that it’s increasing its price for premium members and it’s reducing the ability of free members to use the tool the way that they used to use it. So, as you might expect there’s lots of, what do we use instead of Evernote? I think that a lot of people are taking a fresh look at OneNote. I know I certainly am. It’s free to use. I don’t believe it’s ever going to cost any money, it’s on every single platform, and while I am still finding kind of the analogs to how I would use it with Evernote, I am quite pleased with some of the use cases I am finding.
Another interesting tool down the road to look at is Zoho Notebook. It’s only on the iPhone and Android phones right now, it doesn’t have any other availability, but with what I am seeing so far on my phone, I think that it is a very interesting and intriguing option for note taking. So take a look at both of those, OneNote and Zoho Notebook. Dennis.
Dennis Kennedy: So I like something new to me called HYPERLINK “http://www.degreed.com” degreed.com, as in college degree, so HYPERLINK “http://www.degreed.com” degreed.com, which is a corporate learning tool, but there is actual individual accounts available. And so, Tom and I especially have long had this quest of, is there a way that you can, once you find great things to learn something, can you put it together so you can actually go back and find everything in an organized way, and can you also more importantly share that with people who can kind of follow your trail. So I could say, hey Tom, you want to learn about machine learning, there is this Hanselminutes Podcast and I can kind of collect that all in one place.
So, HYPERLINK “http://www.degreed.com” degreed.com is sort of the latest thing that’s gotten me interested in this. So I mean, Tom, we have done Smart Bookmarks, it used to have like a RSS feed of things that you would share that I subscribe to, is a little way that we shared information.
And there is Evernote, there is like all this sort of sharing, but this is interesting because it’s designed specifically for learning tools, so there is a way that makes it easy for you to find kind of curated learning materials. So it could be YouTube videos, it could be other things that are out there, so it assembles sort of a smaller dataset that’s more useful for learning tools on the Internet.
And then it allows you to gather things together and to create pathways that you can share with other people. So you can say — so imagine that you are working with a paralegal or an associate and you are saying, I want you to keep up to speed on a certain legal topic or certain cases, and you can throw everything that you find into Degreed, where they can look at it and learn from it, and if they found things they could add to it, and it will all be in one place, and you could grow that, and you could share that with other people, bring them up to speed on the topic.
So I have just started experimenting with this and I have had a chance to talk to some of the people in the company as well, and I am really intrigued by the potential of this, so definitely something that I would recommend taking a look at.
Tom Mighell: Yeah, I took a look at it when I saw it in the script, and I agree, I am a big fan of online learning and I think that’s a really interesting and unique way to go about doing it.
So that wraps it up for this edition of The Kennedy-Mighell Report. Thanks for joining us on the podcast. You can find show notes for this episode at HYPERLINK “http://www.tkmreport.com” tkmreport.com. If you like what you hear, please subscribe to our podcast in iTunes or on the Legal Talk Network site, where you can find archives of all of our previous podcasts as well.
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