As we look back over its history, artificial intelligence has been lauded as the “next big thing” for decades. In some ways, it has developed by noticeable leaps and bounds, but in others, its slow, steady integration into our lives has gone largely unnoticed. Dennis and Tom take a look at its progress overall, its particular uses in the legal industry, and then share thoughts on where AI is headed in the future.
As always, stay tuned for the parting shots, that one tip, website, or observation that you can use the second the podcast ends.
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Mentioned in This Episode
A Segment: Checking the Pulse of Artificial Intelligence
B Segment: Checking the Pulse of Artificial Intelligence
Intro: Web 2.0 Innovation, Trends, Collaboration, Software, Metadata, Software Service, Podcasts, Virtual Law. Got the world turning as fast as it can, hear 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: Welcome to Episode 318 of the Kennedy-Mighell Report. I’m Dennis Kennedy in Ann Arbor,
Tom Mighell: I’m Tom Mighell in Dallas.
Dennis Kennedy: In our last episode, we had a great conversation with Joe Camp of Microsoft about the Power platform and tools like Power Automate that many of you already have in your Microsoft 365 subscription, highly recommended. In other news, we have selected our book cover and the new version of our collaboration tools and technology book is off to the printers and we’re told it should be out in the wild by the end of August. In this episode, we’ve been hearing a lot of both how important artificial intelligence has now become and also how it’s really not anything at all and still has a long way to go. So, we thought that made it a good time for us to revisit artificial intelligence or AI as people like to call it and report back on what we found. Tom, what’s all on our agenda for this episode?
Tom Mighell: Well Dennis, in this edition of the Kennedy-Mighell Report, we will indeed take an updated look at artificial intelligence and the current hive cycle as compared to reality on the ground. In our second segment, we’re going to take a look at a category of simple, but wildly helpful collaboration tools online booking calendars and as usual we’ll finish up with Parting Shots at Onetip website or observation you can start using the second that this podcast is over.
But first up, artificial intelligence, AI and it’s 70 plus year story of being the next big thing. We first covered AI as a general topic way back in June of 2015 and then around its application to the practice of law about a year later, July of 2016. We next covered AI in 2020 when the language model GPT-3 became popular. I think we’ll be talking a little bit more about that today so, we wanted to see where we are in 2022, what have we caught up? Where are we still kind of treading water, running in place? Dennis, is there anything really interesting for the legal profession happening today? And if so, why do you think that it starts with GTP-3 or GPT-3, did I say that right? GPT-3, there it is.
Dennis Kennedy: Yeah, GPT-3 which of course as everyone knows stands for Generative Pre-trained Transformer 3 and we’ll talk about that a little bit more about that later. I’m probably going to play the role of optimist in this episode, Tom, is my common role on this podcast and I’ll admit that I’m fascinated by developments like GPT-3 and something new called or relatively new called DALL-E 2, so there are some things happening but I would say that we’re definitely hearing both points of view on AI that there’s too much hype out there and I also think in legal, the term that a lot of vendors are using where they would say, “We have a little bit of AI baked into our products, didn’t really help in the legal tech field.” But I think that we are seeing some developments and we’re also getting a better context of where legal AI fits into the bigger picture of all of AI. I think it’s important from time to time just to get an idea of what the current state of affairs in AI is overall and I think it starts where we always start with AI, Tom, is that no one ever seems to agree on what AI is. It’s probably you and I have some different definitions even as we speak on this podcast.
Tom Mighell: Well, we do and I think you’re right, Dennis, what doesn’t have AI baked in to it nowadays — I would use a different phrase because what I see more often is the phrase “AI powered.” I think that I see a lots of AI powered tools these days, but I feel a little bit numb to it and I’m really not even paying attention, but I think maybe that’s the point and I think we’re going to talk about this a little bit more about whether this might be the best use of AI that, that AI working behind the scenes without ever having to pay attention is probably the best piece, the best way for it to happen and doing things in little mundane ways. I’ll come back to that word mundane a little bit.
In terms of definition, I went out to go and find the best definition that I could find and lo and behold, I found an article that I’m going to put in the show notes that’s called, “These are the best definitions of artificial intelligence you can read today,” and we have definitions from corporations, from the media, from universities, from the government, from Wikipedia and from AI pioneers or experts so, I’m going to put the link in there so you can figure out which definition of AI fits your thinking best. But actually, the author of the article who is a regular author and he’s a podcast host on artificial intelligence, I actually like his own definition which felt very friendly to me which is, “Artificial Intelligence is a type of technology that mimics the human thought empowering machines to act on their own and to perform functions similar to human intelligence such as the ability to perceive, learn, reason and act.” I feel like that’s enough of the future baked into it with that definition and maybe a little bit of — I mean we’re getting there with some of that stuff, but that was the most intriguing definition that I found there. But go look at your own and see which one makes sense. They are all somewhat variations on a theme but no two are the same.
Dennis Kennedy: I’m really intrigued and I unfortunately didn’t get the chance to do this but I want to see if there was an AI that had written a definition of what AI meant because I think that would be a super interesting definition to think about.
Tom Mighell: Well, just hold on a minute and we’ll talk about that later. Actually, you’re spoiling the rest of the stuff I want to talk about.
Dennis Kennedy: But I sort of want the example route and so say what is it that — how are we actually using AI these days if we are? And if people are saying that there’s no such thing as AI, then why would they even be saying that? So, I found that article which from the World Economic Forum which talks about 10 examples of AI that we encounter every day and these are some really interesting things that we take for granted and as Tom was saying, this is AI in the background, and the big one is spam filters, right? Every single day that we’re having the benefit of that article says, “Google says, less than 0.1% of spam makes it past. It’s AI powered filters.” On our phones, we’re seeing voice-activated personal assistants, predictive text, the photo apps, all these things. In banking we’re using fraud prediction, onboarding customers, credit decisions, medicine.
I don’t think that the COVID vaccines would have happened without AI, but it was certainly everything that’s going in — I’d say almost everything going on in the COVID area and treatment and vaccines is going to use some AI, automated cars, anything involving self-driving of our planes and how they get around and how they take us places, the ride-sharing apps and anything that allows us to personalize uses some AI, social media as we know uses AI to put things in front of us including ads, manufacturing, all over the place and then the big one, which is interesting is most of the country is going through a heat wave is regulating the power supply. These are just — these areas, plus there are all these things that in the olden days would be taking as really dramatic examples of AI and that’s optical character recognition and speech recognition, pulling text out of photos, all those sorts of things, which we take for granted were actually magical not that long ago.
Tom Mighell: I will see your long list of examples and I will raise it another long list so, here are the things that I wrote a similar list down and I will take out the ones you mentioned. I think none of these you mentioned, but here are other uses of it. So photorealistic images of people, that website that said that none of these people really exist because they’re all created by AI Clearview AI is a tool that has been in the news a lot lately for being able to recognize faces, not always a good thing.
Instagram has recently tested an AI to verify the age of minors who try to use the service. Just take a look at them and decide, “No, you’re too young,” and boot them off. In addition to AI helping out with COVID vaccine and other things, AI also led to an increase in robotics during the period of time that helped out with social distancing around COVID. The auto industry is using it to quickly inspect vehicles and identify damage parts or maintenance issues. Sports we’re seeing more AI in soccer to detect off sides in baseball, to handle challenges to umpire calls, which is really catching up to where the tennis world has been for a while. Medical use, detecting eye and skin disorders, cancers, clinical diagnosis, measurements. The financial world is using it to detect fraud, to detect money laundering.
There are some concerning ways, I think, that AI is working. If you haven’t seen the news, the story about Alexa. Amazon was developing a new way in Alexa through AI that mimic — I’m going to say, the salacious title of the story and then I’ll go back. It says mimics the voices of your dead relatives. What it does is it actually will learn voices of people that you care about so your child can read, have your grandmother read back to them, which is just all sorts of creepy. We talked in past episodes about the descript application that allows you to create essentially an artificial intelligence model of your voice, so that you can fill in on podcasts or other recordings just by typing the words in and you train it on your voice and it will create text spoken by you, which again, very cool, also subject to abuse. But I just think that the number of things that artificial intelligence can help with these days, there really is we’re getting to no limit on that.
Dennis Kennedy: And I think when you think about legal AI, people are saying, “Well, how can we apply artificial intelligence in the practice of law?” And I would say that with this long list, if you’re a lawyer, you have to have awareness of how AI is being used elsewhere and how it might impact your clients and the matters that you are working on. But you can see how some of these things, even at the basic levels are already starting to move into the tools that we use. I think that, if we think that AI is something where I just ask a question and it gives me the definitive answer and does amazing things, I don’t think our expectations are probably realistic on that. We’ll talk about what is realistic, but I think that when we see all the things that are happening and the likelihood that some of them are going to move over into the legal profession, I think that’s highly likely. And so, I think there actually is a lot happening.
I wanted to talk another aspect of AI, Tom, is I always think that AI has been this moving target. It started out where we’re like, “Well, if we have a computer program that someone can have a conversation with and they think it’s a human, then that’s sort of like an example of AI. Then we said, “No, that’s not it.” Well, if a computer program can beat humans playing checkers, that will be like proof of AI. And we’re like, “Oh no, not checkers. What we meant was chess.” And then we’re like, “No. Just because we beat every chess master, every human chess master, that doesn’t really show that there is AI.” Now we have to go to this game of Go because it’s more complicated and then once the AI won that, then we’re looking for the next thing. This is big moving target. And we’ve also learned, I think over time, that there is this notion that a human together with AI is going to be better at doing things than a human alone or an AI alone and I think that’s an important way of thinking so those are some of the conclusions that I think we see and they’re almost principles that I think we should take forward, especially with this idea that we’re going to be this sort of we work in combination with AI. And that to me is an eye opener in many things, but including the legal profession.
Tom Mighell: Well, no, I think you’re right. I think AI is better at taking available data and synthesizing it, making decisions in what I would call well defined parts of a problem. And then the human is better at understanding the implications of that data.
Which I think, what you say is right is that human and AI is always going to be better and win out than human alone or AI alone. And I think it’s really about how to take the tools that are available and work to that advantage.
Dennis Kennedy: Why more AI now? And I think, it’s this evolution of technology and technological power that’s been coming together. We have software, we have algorithms, we have increases in processing power, we have the cloud, we have enormous data sets and just unbelievably large data sets and that started to make all this happen. I think, that now we’re at this interesting place where so much of the processing can be offloaded off of our devices into the clouds and work with huge amounts of data. We’re starting to see the fruition of that, like everything from self-driving to many of the other examples that we did. It’s not like an easy path and it’s not without difficulties. There are some serious problems that we’re looking at and trying to overcome.
I think we have consistently the issue of do we have enough data to look at and work with for AI to accomplish what it needs to, especially for predicting things, I think that we haven’t seen or really thought through the full impact of COVID on making predictions from historical data patterns. Is the data that we collected between 2020 and now, is that anomalous? Is it indicative of the future? Does what happened, the data that we’re looking at is before 2020, how meaningful is that? Does it have to be adjustments? And we see huge issues around potential bias, geographic, other biases, cultural biases, those sorts of things so there’s a lot to work on, but I think it’s a really exciting time. What do you think, Tom?
Tom Mighell: I agree. I think that as new technology develops, there are always going to be issues with it so none of this is surprising, and COVID has an effect on just about every other impact of our life. Why would it not have an impact on this as well? So, I am not surprised at both the leaps and bounds that artificial intelligence tools have made, and I’m also not surprised by the challenges that go along with it. I think that in a lot of ways and as I said before, one of the interesting things about AI is that a lot of the tools just seem to be running behind the scenes and they’ve been doing so for years without anybody really noticing that that’s happening. Frankly, what we hear about in the news is what the media considers stories about AI that’s surprising or possibly controversial or things that may or may not be a big idea, because I think that bias is an issue to a certain extent. But I’ve also seen stories that say that bias is improving, that they are finding ways to deal with it.
I think that there’s definitely still hype, and I think that the media tends to pile on hype in areas that tend to get more clicks or more information. While I think in the background, AI is making slow inroads and slowly progressing in ways that are — I’m going to use the word they’re more mundane, they’re more boring, and tend, in my mind, to be the sort of thing for which AI was really built.
Dennis, we got to take a break, but any thoughts before we go into the break on where we’re headed and what you’re seeing in terms of AI?
Dennis Kennedy: Yeah, one quick response to something that you said and then sort of three things I would like people to think about as we go through the break. But there’s a great story in the last day or so of a chess playing robot that broke the finger of its seven-year old’s opponent. This made big news and like, “Oh, my God, the terrible AI is the terrible robots.” Then you go like, if you were good at chess as a young child, then the story of you playing with somebody older who got mad that you beat and flipped over the chess board is not surprising at all.
Point one that I would say, as I think about AI these days. I think all the bias stuff is extremely important, but I think that too often we’re judging AI against a mythical perfect human standard or the perfect lawyer or the perfect judge which absolutely does not exist and it doesn’t take many examples to illustrate that point. The second thing is I really like small AI applications, what Kevin Kelly calls artificial smartness, rather than this gigantic universal AI, and that’s your time talked about mundane applications and things that work in the background. I’m also of that school and then, I’m super intrigued by AI as a screening tool that handless simple things and then surfaces what might require more human intervention, but only when necessary, and so, those three points, the mythical perfect human standard, small AI applications and then screening, I think are three big trends to look at in AI and things that you will want to focus on.
Tom Mighell: All right. We’ve got more to say about AI, but first we need to take a quick break for a message from our sponsors.
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Dennis Kennedy: And we are back. What about the current state of AI and the law, Tom?
Tom Mighell: Well, I think we are seeing routinely tools come out that are AI powered, that have a little bit of AI baked into them but I would say that none of them are wowing which is probably good. They’re probably all the mundane ways that they can help people, but I see things like AI tools that will transcribe your meeting for you and provide you with a summary of that meeting. One of the more I think interesting ones is a tool called — I think it’s called Husky, which is a new image search, a new image search tool that will search for your intellectual property on the internet and it will find where it is being used and notify you and provide it in a very interesting way. I’m fascinated how it’s doing that, but I see that — and then, I think that the other area and we’re probably going to turn to talking about more right now is AI in terms of drafting documentation and automating certain types of documents to be created. Those are where I am seeing most of the AI, at least where it’s impacting me and what I’m working with or dealing with. Dennis, how about you? Are you seeing anything in addition?
Dennis Kennedy: There are a number of things, and I think in our last podcast we were talking about in a number of places that Microsoft 365 — Microsoft is starting to build in some simple AI tools. They range from the PowerPoint design tool. There’s some scenario planning tools in Excel. Some of the Power automated tools that we talked about. Also have some AI that we’re able to you as a user are able to grab and use and take advantage of, and those are interesting. You hear about AI in law, in eDiscovery contract, contract management.
And in certain areas where people are doing predictive analytics either in terms of analyzing what court decisions might happen, what might happen in litigation or sometimes in the employment area to help pick up patterns and do things like that. So, there are some things out there. Tom, I was thinking that somebody recently was talking about as a young lawyer was talking about trademarks and how in trademarks, they were wondering how lawyers did trademark law and trademark searches back in the old days, because a lot of trademarks are graphic images and they couldn’t believe that a lawyer would spend hours and hours just like looking through books of pictures of trademarks, and they were wondering what kind of tools they used.
Tom Mighell: The primitive days of old-fashioned lawyering.
Dennis Kennedy: I hate to discount them that people look through like a trademark lawyer often look through books of pictures hoping to spot something. So, it is interesting how things have changed in the example of how I might use AI to identify things that are infringing on my trademark or infringing a copyright or whatever might become more possible, and that’s why I think in terms of screening, because if you have those tools that can go out and find potential problems and then you as the human can look at it, you’re a lot better than saying, “I’m going to spend 90% of my week looking through pictures of trademark images.” But I think that’s going to bring us to two things, Tom, and we’ll probably take them in this order. And the first is GPT-3 which you started to talk about which we can talk about, and then DALL-E too which I find a really interesting variation that unfortunately, despite our best efforts, we were both trying to get on and off of the waiting list for DALL-E too, but we weren’t able to. So, Tom, you started to talk a little bit about what GPT-3 might do, but why don’t I turn that over to you and then I have an example or two myself.
Tom Mighell: We talked about GPT-3 two years ago when it first really hit the news and was something amazing, but just a quick refresher to a certain extent. So, GPT-3 is a product of an organization called OpenAI and they developed this model, and to a certain extent the model tries to predict, it reviews tons and tons and tons of text and tons and tons and tons of formats of that text, and to a certain extent, it’s trying to predict what comes next based on what it’s learned about the world through text. It can be used as a chatbot, a classifier, a summarizer. Some of the things we’ve talked about before, because it understands what those things look like on a textual level. It’s able to recognize them and repeat them and then learn about the types of things that it needs to ask question about. So, for example, Otter.ai is generating meeting summaries from the meetings and that’s because they’re used to seeing the summaries and they’re taking the transcript that they automatically transcribe and then they’re taking that and summarizing it. So, I think it’s really just a tool that is becoming familiar with text and drawing conclusions about it, but I think it’s more. I think it’s becoming more. There’s an article. I’m going to put the link in the show Notes, where a researcher typed a request into GPT-3 and the request was write an academic thesis in 500 words about GPT-3 and add scientific resources and citations inside the text. So, basically it was saying, tell us what you do, tell us what you are, and in two hours it had completed the request and it apparently was coherent enough to be submitted to a well-known journal on machine intelligence. The bad news is I can’t tell you the end of the story because it’s still going on. It was actually assigned to an editor. The abstract is published online. You can go find the abstract of what GPT-3 threat said about itself, but we haven’t actually seen if the actual article will wind up being published. But to me that’s fascinating. It said write an article, and what they wrote was coherent enough, which frankly I’m going to skip ahead a little bit to the concerns that we have about this.
Tom Mighell: But I mean it raises the question, our editors now going to have to put some of question in every time an article gets submitted to say, “Was this written by you?” or “was it written by an Artificial Intelligence?” because I think at some point in time, that may be a legitimate question. Dennis other things that other uses that you’re seeing?
Dennis Kennedy: Yeah, and that’s an interesting thing because you start to say, “Well if it’s like can I be an inventor, can an AI get a patent?” Those sorts of things and you start to say, well, what are we trying to figure out here. If this article is good by some standard then who do we, do we really care who wrote it and how?
Tom Mighell: And real quick, what you are saying that reminds me what is interesting about the article was when they went to go submit it, one of the questions to submit it was, do we have your consent to submit this abstract? And the researcher was like, “I didn’t feel comfortable, giving consent on behalf of GPT-3.” So, she went back and she typed into GPT-3, “do you give consent to co-author an abstract with?” and she listed the name of herself and her co-author and GPT-3 came back and said, “Yes I do.” And I like it feels really weird but that’s the sort of thing is getting credit for, for that from the Artificial Intelligence.
Dennis Kennedy: So, Pieter Gunst and Hannah Konitshek at Legal.io, did this thing on Twitter using GPT-3, what do they just asked it to draft some simple legal documents and then they took a video of it doing it, it was just mind blowing in its way. It’s sort of simple what I call commodity legal documents, you’re like, “Wow, that that was kind of amazing.” And I’ve had some conversations with law professors saying, well, like Tom, saying, “Do we need to be concerned that students are turning in memos and papers and stuff that are read with GPT-3?” you know. And so do we have to think about that because people might already be checking on plagiarism those kinds of things.
So, it does raise some interesting things, but I’m fascinated by, as I say, I’m the optimist on some of the stuff because I’m thinking, we have to go through so much text as lawyers, cases, articles, drafts of things that we read, new stories, all sorts of things, and I’m thinking, can we use a tool like GPT-3 to do that initial screen and give us a summary or what they think is important and will they give us enough to figure out “Hey, here’s what’s going on in these documents, what do I need to follow up on, can I trust it, those sorts of things.”
So, that to me comes super interesting over time, and I am saying in the next, say two to five years, even more so. But Tom, I think we also want to talk about a kind of a different version of that, which is the DALL-E 2 which I’ll let you explain.
Tom Mighell: Coming back to our what we said a minute ago, what you just described about doing that pre-work, being able to go out and find that information and summarize it, I’ll argue again, that’s the mundane, the mundane stuff that we wouldn’t have time for, it’s the commoditized, templated stuff that we would want and then we take that information and apply our analysis and creativity to it again, humans and AI working together. Sorry, I interrupted you. You want to talk about DALL-E 2, do you want to start out or do you want me to start out?
Dennis Kennedy: I want you to start out, because you were kind of looking at DALL-E, a little bit before I was.
Tom Mighell: So, DALL-E is spelled D-A-L-L-E, it’s a combination, it’s a take on Salvador Dalí, the artist and the movie character WALL-E. And what it does is it will take your text instruction and it will create a series of suggested images based on what you write. So, for example, you can say, show me a picture of a Jack Russell terrier as a police officer, and by gosh, you’ll get four pictures of exactly a Jack Russell terrier as a police officer, and no two images look the exact same. I saw one article where they had them, they asked for you can do both photos and digital art as well. You can request those things and I saw somebody asked for a digital art of a bear economist in front of a stock chart crashing, so a bear market basically and came back eight completely different pictures with eight different bears, some of them drawn true to life and some of them drawn like cartoons in various states of despair as the stock market plunges behind it.
And it’s just amazing. The different levels of creativity that this AI can generate on its own based simply on the instructions that you give. You’re not asking it to say, “I want this bear to look sad.” You’re just basically saying, “I want a bear economist with a stock market crashing behind it.” And it’s interpreting that that bear is going to be sad because of that.
I think this really represents a major step forward for creativity. Why hire an artist to draw something for you, if you can have a computer do it in five minutes, why hire a marketing company to design your logo if you can describe it to DALL-E and it’s going to create a passable logo for free.
And the best news is, you can get in on the action. Like Dennis said, we’ve tried, we’re on the waiting list, we started too late, otherwise, we’d be in on it. We will put the link in the show notes to DALL-E 2, it’s on its second iteration, which is even more powerful than the first one. Not sure how long it would take to get off the waitlist and using the service but give it a try.
Dennis Kennedy: Yeah. And like you said, you will start to think in terms of simple things that feel like a little bit daunting because you’re not an artist or well, you need to hire somebody or something. So, your logo example is a great one. And then also, as I think about innovation techniques, a lot of times having something visual can really unlock ideas and if you are able to say, “Well, let’s have DALL-E generate some pictures of involving these concepts or ideas that we have and those pictures may unlock even more other ideas or combinations that you didn’t have.” You could do the same thing potentially I would think with the if your visual with like a case strategy or other things like that. So, really kind of interesting tool as you get into innovation, but it’s a classic example of where you’re saying like, “Well, I’m not sure there’s any AI in law or I don’t see the implications of AI for law” And you’re going like, “I don’t know.” And there’s some interesting tools out there that if you start to think about and experiment you may find something that really helps you.
So, Tom, I know we need to wrap up and I think it’s pretty clear, there are things that really were encouraged by and some things give us concerns, but what do you think our listeners should do after hearing this?
Tom Mighell: Well, I mean, I think that the best thing is to take a look at the tools you use and try and figure out, or try and learn, is there a little bit of AI baked in? So, to speak? Is there any power there? Learn how the tools you use already may be influenced by Artificial Intelligence to get an appreciation for it.
Tom Mighell: I think it’s really and this is kind of one of those areas that keeping up-to-date and abreast of the knowledge, I think is a good idea because as more tools become available you will find and there will become more use cases for lawyers to use. And I think it’s only going to get more interesting, but there are concerns, we have concerns about fraud. We’ve got deepfake issues. There’s I think a concern about seeing artificial intelligence as a panacea to our problems when it’s really just a tool. If you saw the news in the past week when we’re recording, Google fired one of its engineers because the engineer is now convinced that the AI that they created it’s called LaMDA is a sentient being that has a consciousness and Google was very quick to say, “No, no, no, that’s not true, it does not, it’s not sentient.” And then they fired the guy. So, we still have a way to go on realizing what –
Dennis Kennedy: Which is exactly what a sentient AI would do.
Tom Mighell: That’s exactly right. So, there’s still some concerns but I think both Dennis and I are rather bullish on the whole concept and we think you should just keep up with what, what’s going on, and because I think it’s only going to get more interesting.
All right before we move on to our next segment, let’s take a quick break for another message from our sponsor.
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Tom Mighell: And now let’s get back to The Kennedy-Mighell Report. I’m Tom Mighell.
Dennis Kennedy: And I’m Dennis Kennedy and as I mentioned the new version of our collaboration tools and technologies book is off to the printers. One thing we’ve always covered in the book is very simple single-purpose tools that can really make your life easier when you’re working with other people. And so, I think one of the most annoying problems in collaboration is scheduling meetings which of in the old way of doing things which is still very, very common it can involve the exchange of what feels like hundreds of emails trying to set up time for meeting. So, there are tools out there like Doodle, Calendly and Microsoft bookings that can address this issue and actually make you feel like a great weight has been taken off your shoulders and you can breathe a little fresh air about scheduling meetings. So, I recently made the move to Calendly which I say I probably did too late and I should have done a long time ago because I’m become a big fan almost immediately. Tom, am I overstating how liberating these simple tools are?
Tom Mighell: No, you are not overstating them. These are great tools and we’ve been saying that for a long time. I’m sorry it took you so long to start using them. I’ve had been using FindTime and Doodle for quite a while now but I’m going to almost sound like the pessimist on this particular segment because I don’t think everybody feels the same way about these tools. Interestingly, there was kind of it’s interesting that you’re using Calendly because there was sort of a minor controversy a few months back where a tech investor got into a big brouhaha on Twitter by posting that that he thought that a tool like Calendly was the worst form of capitalism, the most naked form of capitalism available because telling me that you’re sending me your Calendly link is telling me that your schedule is more important than mine. I’m not sure I understand that and I’m not sure I agree with it but there is pushback on tools like that. What I think is the more interesting negative to it is I don’t think that these tools work in all contexts. I think that scheduling tools work best in two main situations, one-on-one meetings and groups where the people know each other. If you don’t know who you’re trying to set a meeting up with, or you want something or it’s a large group of people who you don’t know, getting them to use a tool and all use it correctly I think is difficult. I’ll say in a work context it’s very hard to do. I work and I conduct a lot of meetings with different departments with my clients. I’ve never met them before and it would be a total bureaucratic nightmare to send out Doodle polls or Calendly links to a lot of people who don’t even know me. They get a link from me saying, “Please schedule a meeting with me asking them to find time on my calendar.” They’d flat-out refused to do that. They just wouldn’t even be an issue for them. It doesn’t work well in that situation. So, I think that there’s a time and a place to use them and when that works, it works flawlessly and it is brilliant. But I think that they’re also, I hate to say it, some people who are still challenged by the tools. You know, maybe not something is as simple as Doodle. Maybe Calendly is also easy, but there are some tools where I will get a puzzle email from someone when I try to use it. I try to use FindTime from Outlook, and I still think that’s a pretty easy tool, but I still get questions saying, “What am I supposed to do” because it’s a wholly new concept for them. So, they’re still again, we still have lots of people who have not adopted it. So, I guess for many that liberating feeling is probably still ways off, but I’m glad that at least you and I are enjoying it, Dennis.
Dennis Kennedy: So, you make a couple of great points, Tom is one is, I think the Calendly which shows your available times on your calendar and essentially less people grab them and then Zoom appointment gets scheduled with the reminders, text reminders, email reminders. Everything’s all set up and done for you. And I think that works amazingly well for where we call appointments. If you’re trying to coordinate a big group meeting, I don’t think it works so well. And Doodle, something like Doodle can work. I think that you said there are different types of people and people like different things. We talked about this notion of co-collaboration in the book and I think I see these tools as like this way to accommodate to people. Make yourself easier to work with, and make things a little bit easier for them. Where I get the pushback or the telephone people. We would say,” I don’t like Zoom. I don’t like that I have to schedule a time. I just want to pick up the phone and call you.” And they’ll say, “Can I just call you?” And you go, “Yeah but what’s going to happen is my phone is going to send you right to voicemail because it doesn’t recognize your number.” Like can’t we just do like this appointment thing and make it easier
So, I think that as you said, “if you find the right purpose” and that’s the whole, probably one of key themes and everything we’ve done in the podcast and certainly in the collaboration book is like, “what is the job that you need to get done?” So, understand that really well and then try to make it easy for people who work with you. These tools because they fix something that is really frustrating and annoying can be really helpful. And like I said, my experience with Calendly is great because now it’s super easy for people to grab time on my calendar at their convenience because that show is open, an open slot they can grab. So, now it’s time for parting shots. At OneTip website or observation, you can use the second on this podcast in. So, Tom, take it away.
Tom Mighell: So, in keeping with my theme for this episode, my parting shot is what I would consider to be a little bit mundane but it’s definitely something that I’ve never taken advantage of and then I think more people could benefit from. We recently had a new addition to our home, not a young addition, but young adult who’s needing a place to stay while getting ready for having a life and being successful on their own. And we realized that we didn’t really want to be necessarily giving this person the key to the house so they can have all the time and or we didn’t want to have to manage lots of keys floating around. And so, we round up getting the Lock Schlage in code plus Smartlock. There are lots of smart locks out there so I’m not this early recommending this over any other particular Smartlock, but this solves two major or three or four major problems for us which is one, we walk the dogs in the morning. I usually bring a key with me. Several times when I pulled the stuff out of my pocket, the key has gone missing on some street where I was walking the dog and getting home without that key is a pain. Now, all I have to do is plug in a code when I get home, and I’m able to walk in. If one of us forgets the key the other one can always put up or gets locked out for some reason. One of us can pull up our phones and automatically unlock the door. We can give our new guest, our new resident a digital code that they can use that expires immediately, or that can be kept for a while. And so, you know, it’s just a very simple and elegant tool to use instead of keys and I am quite enjoying the Smartlock. I think it is a good addition and definitely solves the problem that we had around access to our house, Dennis.
Dennis Kennedy: So, I have a book and a companion website. The book is called “Work Clean” by Dan Charnas. Sometimes, there’s a hardback and a paperback and sometimes it’s called everything in its place. The website is workclean.com. So, this is an approach to personal productivity, which Tom and I are always experimenting with and this takes the notion of mise-en-place which is a term that chefs use that everything has its place and you work and it’s in a very clean way and applies it to the rest of your life. It’s fascinating approach. I took some ideas that I’m already using out of it and thoroughly recommend it if you’re interested in improving your personal productivity.
Tom Mighell: And so, that wraps up this edition of the Kennedy-Mighell Report. Thanks for joining us on the podcast. You can find show notes for this episode on the Legal Talk Network’s page for the show. If you like what you hear, please subscribe to our podcast in iTunes, on Legal Talk Network site or in your favorite podcast app. If you like to get in touch with us, remember you can always find us on LinkedIn, on Twitter or remember we love to get your voicemails. Send us a voicemail at (720) 441-6820. So, until the next podcast, I’m Tom Mighell.
Dennis Kennedy: And I’m Dennis Kennedy and you’ve been listening to the Kennedy-Mighell Report, a podcast on legal technology with an internet focus. If you like what you heard today, please write us on Apple podcasts and we’ll see you next time for another episode of Kennedy-Mighell Report on the Legal Talk Network.
Male: Thanks for listening to the Kennedy-Mighell Report. Check out the Dennis and Tom’s book, “The Lawyers’ Guide to Collaboration Tools and Technologies, Smart Ways to Work Together. From ABA Books or Amazon. Enjoy this every other week for another edition of the Kennedy-Mighell Report only on the Legal Talk Network.