Mark Watson's artificial intelligence and Lisp hacking blog

I am moving back to the Google platform, less excited by what Apple is offering

2024-09-19T13:48:00.004-07:00

I have been been playing with the Apple Intelligence beta’s in iPadOS and macOS and while I like the direction Apple is heading I am getting more use from Google’s Gemini, both for general analysis of very large input contexts, as well as effective integration my content in Gmail, Google Calendar, and Google Docs.

While I find the latest Pixel phone to be compelling, I will stick with Apple hardware since I don’t want to take the time to move my data and general workflow to a Pixel phone. The iPhone is the strongest lock-in that Apple has on me because of the time investment to change.

The main reason I am feeling less interested in the Apple ecosystem and platform is that I believe that our present day work flows are intimately wrapped up with the effective use of LLMs, and it is crazy to limit oneself to just one or two vendors. I rely on running local models on Ollama, super fast APIs from Groq (I love Groq for running most of the better open weight models), and other APIs from Mistral, OpenAI, Google, Anthropic, and other providers.

Except for accepting part time work, I am mostly retired and for my now more limited development requirements, I am using platform agnostic web based systems like Replit with their AI coding assistant and Google Colab with Gemini assistant.

I have a lot of excellent Apple hardware that I will use until it breaks and then decide on replacement gear based on cost performance. For years I have loved the Apple ecosystem but in modern times things change so quickly and I prefer to not being strongly loyal to any particular provider.

Getting closer to AGI? Google's NoteBookLM and Replit's AI Coding Agent

2024-09-18T14:07:00.007-07:00

Putting "closer to AGI?" in a blog title might border on being clickbait, but I will argue that it is not! I have mostly earned my living in the field of AI since 1982 and I argue that the existence of better AI driven products and the accelerating rate of progress in research, that we are raising the bar on what we consider AGI to be.

I have had my mind blown twice in the last week:

Today I took the PDF for my book "Practical Artificial Intelligence Programming With Clojure (you can read it free online here) and used it to create a notebook in Google's NotebookLM and asked for a generated 8 minute podcast. This experimental app created a podcast with two people discussing my book accurately and showing wonderful knowledge of technology. If you want to listen to the audio track that Google's NotebookLM created, here is a link to the WAV audio file

Last week I signed up for a one year plan on Replit.com after trying the web based IDE for Haskell and Python coding. After using the platform for a few days I used its AI coding agent to rewrite my Clojure web app CookingSpace.com in Javascript, HTML, and CSS. I estimate that doing this by hand would have taken me at least 30 hours, and I had something written and publicly deployed in less than an hour. This process would have been quicker but initially I asked for features like user accounts that I later decided I didn't want so I had to iterate with the AI coding agent to remove working functionality.

Why claim these two well engineered products make me feel that we are closer to AGI?

To be fair what makes NotebookLM and Replit AI coding agent so impressive is the product design and infrastructure that uses LLMs under the hood. Still, it is very difficult to say that the functionality of both these systems is not now superior to most humans.

I feel that the argument over AI and AGI gets dragged down by discussions of sentient AI taking over the world of somehow controlling humans. The thing to worry about is how people use AI.

I find it difficult to view AI as anything but a set of advanced tools, something to augment human productivity. In my view, advanced AI driven tools and products are very close to what I would call AGI.

New OpenAI gpt-o1-preview and gpt-o1-mini and one week experience with Replit.com AI Coding Agent

2024-09-13T10:23:00.004-07:00

I have only spent a short while experimenting with the ne gtp-o1 models: so far very impressive for science, math, and instruction following. You need a ChatGPT Plus account to try it, or you can perform rate limited queries for half the monthly cost using Abacus AI

The thing I am most impressed with (this week!) is the Replit.co AI coding agent that after briefly trying it I pre-paid for a one year subscription. I quickly rewrote a complex Clojure web app I wrote 12 years ago in JavaScript making it much less expensive to host CookingSpace.com (new JavaScript version)

I gave a live demo of Replit AI in my weekly AI demo and group chat. Please join: Mark Watson's informal AI presentations and group chats

Topics: Recipe: Mark’s African Stew, and converting my Clojure CookingSpace web site to JavaScript

2024-09-12T15:06:00.000-07:00

I wanted to convert my server side web site CookingSpace.com to mostly client side JavaScript. I used the Replit.com AI coding agent to do this, and while this is a subject for another blog article, I showed the Replit coding AI a snippet of my JSON recipes and nutrients file, and described in a few paragraphs the functionality of the new web site.

I want to share a recipe that I created:

Mark’s African Stew

Ingredients

Optional: 3/4 to 1 pound of Lamb shoulder, deboned and cut into cubes, leaving some fat on the meat
2 tablespoons of any non-seed oil (Avocado Oil is good). (Avoid seed oils like Canola, Corn, Soybean, Sunflower, etc.)
1 brown onion, coarsely chopped
1 egg plant, partially skinned, chopped into bite sized pieces, and heavily salt in a strainer. Before cooking, rinse well to remove excess salt!
ground coriander (at least 1 teaspoon)
Cumin (at least 2/3 teaspoon)
paprika (1 to 2 teaspoons, depending on how you like the taste of paprika)
ginger powder ( at least 1/2 teaspoon), or, even better use finely chopped fresh ginger
pealed and finely chopped)
cinnamon (1/4 to 1/4 teaspoon depending how much you like the taste of cinnamon)
black pepper (at least 1/3 teaspoon - nutritionally pairs well with the turmeric)
turmeric (bout 1/4 teaspoon), or, even better use fresh turmeric root peeled and finely chopped.
fresh chopped garlic (or garlic powder) added to taste. I use about 3 garlic cloves, you probably want less.
Something hot and spicy: dried chili peppers, or fresh chopped chili peppers, or hot chili sauce - whatever you like, adding enough ‘hot’ for your tastes.

1 cup chicken broth, or vegetable broth if you are not using the optional meat.
4 cups water, or more in stew dries out while cooking
1/4 pound of lentils
2 or 3 celery stocks, coarsely chopped. Use the green tops also! Use the root also.
1 14oz can of diced tomatoes
1 16oz can of cooked garbanzo beans, using water in can
2/3 cup chopped fresh cilantro, I use about half the stems (finely chopped)
Small pasta noodles, I use a very small amount as an accent
Optional: a table spoon of honey added after heat is turned off.
Juice from 1 fresh lemon (optional, but…)

Directions:

Brown the lamb in about 2 tablespoons of any non-seed oil (Avocado Oil is good) and remove, leaving oil and the small amount of fat from cooking the meat.

Sauté the onion

Turn off heat and add rinsed lentils, prepared eggplant (rinse salt off before adding to pan), and all spices.

Cook covered under low heat for about 20 minutes.

Add everything else: canned garbanzo beans, chopped celery, cilantro, diced tomatoes, and return the meat to the heavy pan, cover and simmer for a while. Cook for at least 10 minutes before adding pasta:

Add the small amount of plaster noodles last, calculating how long they should cook by doubling the time that you would cook them in boiling water.

When the pasta tastes done, cover the heavy pan, cook another minute, then turn off heat leaving the heat and leave the pan covered for 5 to 20 minutes.

Stir in fresh lemon juice and serve. After tasting at the table, add salt if necessary.

Code and notes from my recent talk: Exploring the Future of AI: Introduction to using LLMs using Python

2024-09-12T14:55:00.000-07:00

Topics: Large context prompts with LLMs vs. RAGs using embeddings vector stores. How to avoid LLM hallucination. 3 code demos.

started an informal code demo and group conversation Meetup group (link) and today I gave a fifteen minute code demo followed by a conversation with the attendees. Here is a GitHub repo with the code examples: https://github.com/mark-watson/talk2_LLM_Python_intro
Here are my talk notes:

Exploring the Future of AI: Introduction to using LLMs using Python
Riff on ‘AI grounding’ and how LLMs help: LLMs, trained on vast amounts of text, excel at recognizing patterns and providing contextually relevant responses. They mimic grounded understanding by referencing large datasets that encompass a variety of real-world scenarios. For example, they can infer meanings from complex contexts by drawing on their training data. When LLMs are integrated with other modalities, such as vision or audio (e.g., vision-language models), the grounding improves. These models can associate text with images or sounds, making the connections more robust and closer to a human-like understanding of concepts.
Tradeoffs between using large context LLMs: where a large body of text is added to a query prompt, to the alternative approach of breaking multiple documents into many separate chunks of text, calculating an embedding vector for each chunk, and then storing the chunks and their associated embedding vectors ins a vector data store.
Long-Context LLMs: designed to support processing large blocks of text, often an entire book, within a single prompt. These models can accommodate extended sequences of text, enabling them to consider more context at once. This is particularly useful for tasks that require maintaining continuity over long narratives or documents. However, long-context LLMs have limitations, such as performance degradation when the context becomes too long, which can lead to reduced accuracy in generating or retrieving relevant information. These models are also computationally expensive, as handling extensive sequences demands significant resources.
On the other hand, vector stores (or vector databases) work by converting text or other unstructured data into high-dimensional vectors using embeddings. These vectors are stored and can be retrieved based on their similarity to a query vector, allowing for efficient semantic search across vast datasets. This approach provides a form of “long-term memory” for LLMs, enabling them to access and retrieve relevant information from large collections of documents without needing to process the entire context at once. Vector stores are particularly useful in retrieval-augmented generation (RAG) systems, where they help the model to find and focus on the most relevant information, improving both efficiency and accuracy .
In essence, while long-context LLMs attempt to handle extensive information within the model’s processing window, vector stores offer an external memory solution that complements LLMs by efficiently managing and retrieving relevant information from larger datasets.

What about LLM hallucinations?
Long context windows and retrieval-augmented generation (RAG) data stores significantly reduce LLM hallucinations by improving the model's access to relevant and accurate information during the generation process.
1. Long Context Windows: When LLMs are equipped with long context windows, they can process and retain more information within a single session. This allows the model to maintain continuity and consistency over extended text, reducing the chances of fabricating information that doesn't align with the given context or user query. By having access to more surrounding context, the model can generate more coherent and accurate responses that are anchored in the actual input data.
2. RAG Embedding Vector Data Stores: In a RAG setup, an LLM is paired with a vector store that holds a vast amount of pre-processed, structured information. When a query is posed, the model retrieves relevant documents or data snippets from this store, which then informs the generation process. This retrieval step grounds the model's output in factual data, effectively reducing the likelihood of hallucinations. Since the model can rely on precise and contextually relevant information, it is less prone to generating plausible-sounding but incorrect or nonsensical content.
Together, these approaches enhance the reliability of LLM outputs. Long context windows allow the model to consider more of the input in a single pass, while RAG ensures that the model has access to verified information, leading to fewer instances of hallucination and more trustworthy results.

My Dad's work with Robert Oppenheimer and Edward Teller

2023-08-28T07:23:00.001-07:00

Robert Oppenheimer and Edward Teller facilitated my Dad getting a professorship at UC Berkeley when I was 3 years old. Oppenheimer left Berkeley but Teller was a good friend of my father and I remember him being in our home.

Three weeks ago, my wife and I were just leaving to see the new Oppenheimer movie when my Dad called. He mentioned that when I was in grade school he was invited to give a talk at Princeton. After his talk Oppenheimer talked with my Dad and invited him to have dinner at his house. My Dad said Oppenheimer was not well (I think he died of throat cancer soon afterwards) but his wife Kitty carried the conversation.

My Dad, Ken Watson, passed away 10 days ago on August 18, 2023.

Time and Attention Fragmentation in Our Digital Lives

2023-03-12T16:29:00.001-07:00

As humans we have evolved over a few million years to be both attentive and reactive to danger, live in social communities, and spend much of our time being in the present moment gathering and eating food and socializing.

The behavior of rapidly changing short attention to content on social media, too many good short form things to watch on streaming video entertainment platforms, are all rewiring our brains in an unnatural and unhealthy way.

I fight back, but in really simple ways that entail little ceremony:

Almost every morning I spend 30 minutes scanning Hacker News (about 10 minutes), Apple News (about 5 minutes), and the remaining time on Twitter and Mastodon finding interesting new (mostly tech) things. I make notes in a temporary Apple Note: links of things I may want to research, try, or simply read that day. I like to get this all done at once, and then not feel like I need to interrupt my activities during the day to “catch up” on what is happening in the world.

In a way, it seems like 30 minutes a day, summed up over 365 days in a year, is a frightening amount of life spent on what I would consider “indexing.” In a typical day, I might spend between 1 and 3 hours taking long attention span meanderings over stuff on my daily (temporary) Apple note. While I feel kind of bad about the 30 minutes of chaotic short attention span “discovery and indexing” time each morning, I don’t feel bad at all, in fact the opposite I feel good about spending more time during the day diving more deeply into the few things on my daily list.

Another thing I feel very good about is spending long periods of time socializing with friends and family, reading fiction (and sometimes technical books) or watching a single good movie in the evening.

ChatGPT as part of the evolution of programming languages

2023-02-02T15:39:00.007-07:00

In the 1940s von Neumann and his colleagues created conceptual models for computer architectures that were oriented toward the engineering problems of building computing devices, and not for making it easier for humans to write programs. The Lambda Calculus and also the design of the Prolog programming language are the first real efforts that I am aware of to place emphasis on how we humans think and solve problems.

I had a thought earlier today that I keep coming back to: there are concise programming languages that can be more difficult to write code with, but once done the code is more valuable because of its conciseness that yields better readability.

I have been fascinated by, and use Copilot and ChatGPT to write code and sometimes it works well enough. What will the effects of ChatGPT and future LLMs be on the popularity of niche languages like Prolog and APL?

All things considered I would often rather have a concise program in Prolog or a flavor of Lisp than a much larger program written in a verbose language (e.g., Java). If I can describe a problem and generate a quality program in any language, then I will ask for generation in my favorite languages.

Because of its possible utility for so many tasks, ChatGPT and future similar systems really seem like programming environments to me.

Collecting textual context data for a problem, appending a series of questions to be answered, and sending the resulting text off to ChatGPT also seems like programming to me.

Elon Musk & Twitter, Tech job market, my writing projects

2022-11-05T08:30:00.000-07:00

I find it sad that Musk's purchase of Twitter is turning out so badly. I read that advertisement pre-sales for next year are very low, thus the urgent need to cut expenses. I don't dissagree with Elon Musk's original idea of having an uncensored platform, but the execution is not good. My best wishes to everyone at Twitter (and other tech companies) who have lost their jobs recently. The job market was crazy-good for a few years, and now I expect it to more like after the 2000 dot-com-crash, at least for a few years.

I advise people to take a different approach to managing their careers. As fantastic as online (often free) classes are for teaching useful stuff like machine learning, front end development, etc., this has also greatly expanded the global talent pool. Now more than ever, I advise learning through doing your own projects. I have literally done this myself for the last 40 years: I spend my own time experimenting with tech that both fascinates me and might be useful for my employer. Take control of what you want to learn and work on even if initially it is unpaid work.

I have been using my Mastodon account https://mastodon.social/@mark_watson but I have plans what so ever to give up Twitter (you can follow me at https://twitter.com/mark_l_watson). I rely on Twitter to get tech news, thanks to the awesome people I follow. I am working hard to also follow awesome people on Mastodon.

About 12 years ago, I used LaTex to write a semantic web book, but in 2 editions: Common Lisp and Java. It was very cool to have 2/3 of the manuscript common text, and about 1/3 unique to the programming language. For the last two evenings, I have been revisiting my old manuscript materials because I am thinking of updating the material and also creating additional editions for more programming languages: Python, JavaScript, and maybe Swift and Prolog.

I had forgot how cool TeX and LaTex are. It was very easy to start working with again, even after a 12 year gap.

Not really retired 😀

2022-10-01T10:45:00.000-07:00

I read with some humor my last blog post from 6 months ago, saying that I was retired. Ha! As I mentioned 6 months ago, my wife has chronic health problems but those have stabalized, and life is now fairly good. I did start a very much part time (15 hours/week) advisory gig with Mind AI about 4 months ago. Enjoyable work on an interesting product. I am not performing any substantial development work, rather spending most of my time as an architect and advisor. I am 71 years old, and leaving heavy lifting development work to younger and more energetic co-workers is for the best.

I have a huge backlog of writing projects, mostly on hold for the moment because of my work at Mind AI:

A new book "Artificial Intelligence Programming in Python: Exploring the Boundaries of Deep Learning, Symbolic AI, and Knowledge Representation".
Edits for my Common Lisp book, adding new examples.
Ideas and some new code for my Swift AI book.

For the last year I have been basically giving away my recent eBooks for free on Leanpub by setting the minimum price to $0.00. I stopped doing this today. Now all of my recent eBooks are available for free online reading on my website https://markwatson.com and readers have to pay for PDF/ePub/Kindle versions on Leanpub.

I have been very happy to get 5 visits to my home in Sedona this year from old collegues, including my boss from 40 years ago and a recent co-worker from Olive AI. Great fun!

I also have a new hobby: I joined the Northern Arizona Audubon Society. Yes, I am now a bird watcher! I have some experience at this bird watching activity: I have had my pet parrot for 20 years. Looking at wild birds is fun also, and the birding hikes are guided by experts so this is a learning experience.

I retired yesterday: my list of things to do in retirement

2022-03-05T06:44:00.002-07:00

What does an intelligent person do in retirement? That is a question of individual tastes but I will share my list of 20 things.

Yesterday was my last day working on a recommendation model at Babylist. Babylist is a great company to work for but I decided to retire in order to spend more time helping my wife who now has chronic health problems. When I write books, my wife enjoys editing my work so we will keep doing that. I also plan on being a gentleman computer scientist by working on open source deep learning applications and semantic web/linked data tools and applications.

I may end up not doing all of these things, but generally I plan on spending more time on current interests and starting some new hobbies:

Retirement Activities

Join an Internet Chess club **DONE**
Get a fishing license
Video Games
Improve my cooking/recipe web site
Reading
Release new editions for my 3 most popular eBooks
Practice guitar, Native American Flute, and didgeridoo
Eco-box vegetable garden
Set up my model trains
Play online Go. **DONE** online-go.com
Learn to use the Ham radio (listen only, no license) that my son in law Josh gave me
Photography
Wednesday and Saturday morning bird watching tours at Red Rock State Park
Kayaking
Take more road trips with Carol and friends
Using my new meat smoker
Hiking with my friends
Marksmanship: spend more time at the shooting range (I own a 22 caliber target pistol)
Meditation
Start taking flying lessons again

I may not do all of these activities but I found it useful to put some thought into what I want to now spend my time on.

My productivity hacks

2021-09-21T08:56:00.002-07:00

Like most people, there are many more things that I would like to do than I have time to do. I believe that learning to identify action items that simply should not be done is valuable, but not easy to do. I am mildly attention deficit in the sense that I can only think about or attend to one thing at a time. For a computer scientist, this has been a super power, but is personally inconvenient. I keep 3 TODO lists:

TODO high priority - I tend to have 1 to 3 things on this list. I time box my activities so this list is the actions that I will rotate through. I usually work in 60 to 90 minute sprints, but for deep coding this may be 3 or 4 hours.
TODO open - everything that I would like to do, but a lot of stuff on this list gets deleted with no further effort (the all important decisions on what not to do).
TODO done - instead of deleting completed actions on "TODO high priority" I cut the text and paste the action text to the top of this list.

I really like the Freedom.to web site and app blocker. I feel like I am setting parental controls on myself. I keep a list of fun but not very useful web sites and apps (e.g., Hacker News, Twitter, Reddit tech groups, and Facebook) that can only be accessed in the early morning, briefly during lunch, and in the evening times. I sometimes temporarily unblock sites if I want to quickly post something to social media.

I place no limits on the time I spend on meditation, hiking, kayaking, cooking + meals, and reading good books. I try to limit watching streaming media to a few shows and also movies I watch with my wife and/or friends.

DBPedia Natural Language Interface Using Huggingface Transformer

2021-03-24T10:36:00.006-07:00

I prototyped a simple natural language question answering demo in about 90 minutes. I accept a query like “where does Bill Gates work?”, find the likely URI for Bill Gates, collect some comment text for this DBPedia entity, and then pass the original query to the transformer model with the “context” being the comment text collected via a SPARQL query. I run this on Google Colab. Note that I saved my Jupyter Notebook as a python file that is in the listing below. Note the use of ! to run shell commands (e.g., !pip install transformers).

# -*- coding: utf-8 -*-
"""DbPedia QA system.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1FX-0eizj2vayXsqfSB2ONuJYG8BaYpGO

**DBPedia Question Answering System**
Copyright 2021 Mark Watson. All rights reserved. License: Apache 2
"""

!pip install transformers
!pip install SPARQLWrapper

from transformers import pipeline

qa = pipeline(
    "question-answering",
    #model="NeuML/bert-small-cord19qa",
    model="NeuML/bert-small-cord19-squad2",
    tokenizer="NeuML/bert-small-cord19qa"
)

!pip install import spacy
!python -m spacy download en_core_web_sm

import spacy

nlp_model = spacy.load('en')

from SPARQLWrapper import SPARQLWrapper, JSON

sparql = SPARQLWrapper("http://dbpedia.org/sparql")

def query(query):
  sparql.setQuery(query)
  sparql.setReturnFormat(JSON)
  return sparql.query().convert()['results']['bindings']

nlp_model = spacy.load('en')

def entities_in_text(s):
    doc = nlp_model(s)
    ret = {}
    for [ename, etype] in [[entity.text, entity.label_] for entity in doc.ents
        ]:
        if etype in ret:
            ret[etype] = ret[etype] + [ename]
        else:
            ret[etype] = [ename]
    return ret

def dbpedia_get_entities_by_name(name, dbpedia_type):
  sparql = "select distinct ?s ?comment where {{ ?s <http://www.w3.org/2000/01/rdf-schema#label>  \"{}\"@en . ?s <http://www.w3.org/2000/01/rdf-schema#comment>  ?comment  . FILTER  (lang(?comment) = 'en') . ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> {} . }} limit 15".format(name, dbpedia_type)
  #print(sparql)
  results = query(sparql)
  return(results)

entity_type_to_type_uri = {'PERSON': '<http://dbpedia.org/ontology/Person>',
    'GPE': '<http://dbpedia.org/ontology/Place>', 'ORG':
    '<http://dbpedia.org/ontology/Organisation>'}

def QA(query_text):
  entities = entities_in_text(query_text)

  def helper(entity_type):
    ret = ""
    if entity_type in entities:
      for hname in entities[entity_type]:
        results = dbpedia_get_entities_by_name(hname, entity_type_to_type_uri[entity_type])
        for result in results:
          ret += ret + result['comment']['value'] + " . "
    return ret

  context_text = helper('PERSON') + helper('ORG') + helper('GPE')

  print("Answer from transformer model:")
  print("Original query: ", query_text)
  print("Answer:")

  answer = qa({
                "question": query_text,
                "context": context_text
               })
  print(answer)

QA("where does Bill Gates work?")
QA("where is IBM is headquartered?")
QA("who is Bill Clinton married to?")

The output looks like this:

Answer from transformer model:
Original query:  where does Bill Gates work?
Answer:
{'score': 0.31679803133010864, 'start': 213,
 'end': 222, 'answer': 'Microsoft'}
Answer from transformer model:
Original query:  where is IBM is headquartered?
Answer:
{'score': 0.8704459071159363, 'start': 115, 'end': 131,
 'answer': 'Armonk, New York'}
Answer from transformer model:
Original query:  who is Bill Clinton married to?
Answer:
{'score': 0.00018714569159783423, 'start': 480, 'end': 505,
 'answer': 'former secretary of state'}

I have a new job helping to build a Knowledge Graph at Olive AI

2020-10-22T16:41:00.000-07:00

I retired (my last job was Master Software Engineer and the manager of a deep learning team at Capital One) a year ago April and was enjoying time with friends and family, doing personal research in hybrid AI, lots of writing, and volunteering at our local food bank. I stopped my volunteer work with COVID-19 and welcomed the opportunity last month to start work at Olive AI working on a very strong Knowledge Graph team. I believe in their mission and the work and the people are great!

It is refreshing to leave the deep learning field, at least for a while. My heart is in developing stronger AI that can explain its actions and adapt flexibly to help people in their lives. I always take a humans-first stand on technology. AI systems should help us get our work done efficiently and remove tedium, allow us more time for creative activities, and generally enjoy our own humanity.

I have tried to take advantage of extra time during the COVID-19 pandemic

2020-07-08T06:36:00.000-07:00

My wife Carol and I have been practicing social distancing and wearing masks for shopping for over 5 months now. Welcome to the new normal and a crazy world in which entertaining and seeing friends is done by meeting in people's yards and everyone bringing their own "meal in a bag."

I enjoy writing so I have been updating my recent books, starting with Loving Common Lisp, or the Savvy Programmer's Secret Weapon and A Lisp Programmer Living in Python-Land: The Hy Programming Language. These are free to read online and licensed with Creative Commons Share and Share Alike, No Commercial Reuse, so you can also find copies on the web (hopefully up to date copies!).

Last month I started a much larger project: I have not updated my book Practical Artificial Intelligence Programming With Java since the fourth edition was published in 2013. I have discarded a lot of older material like exert systems, and have three new chapters on the semantic web and also a new chapter on deep learning. I also copied the material on anomaly detection from my Power Java book that is now discontinued and updated that material. Lastly, I am revisiting how readers install, run, and experiment with the code examples. I am still using maven but I am being more consistent, all of the examples are also installable libraries, and now some of the examples use the libraries developed in other examples.

For fun, I have been buying more material for my Oculus Quest VR device. Favorites include ping pong, racket ball, the Star Wars Darth Vader Immortal 3 volume set, and I enjoy a lot of 3D art that people post.

For exercise I try to hike every morning from 5:30am to about 6:30am (I live in the mountains of Central Arizona and even at high altitude it gets warmer later in the day). My wife and I cancelled our gym memberships so I bought myself some weights that I keep in my home office. I am a huge fan of the Apple Watch and I use it to track health and fitness activities.

I hope that you, dear reader, are doing well in these crazy times we are living in.

Custom built SBCL and using spaCy and TensorFlow in Common Lisp

2020-02-18T09:33:00.000-07:00

Here are some of my of my recent notes that might save you some time, or teach you a new trick.

I have had good results using the py4cl library if I wrap API calls to TensorFlow or spaCy in a short Python library that calls Python libraries and returns results in simple types like strings and dictionaries. I just committed a complete example (Python library and Common Lisp client code) to the public repo for my book Loving Common Lisp, or the Savvy Programmer's Secret Weapon that will be added to the next edition of my book. Here is a link to the subdirectory with this new example in my repo: https://github.com/mark-watson/loving-common-lisp/tree/master/src/spacy

I frequently make standalone executable programs using SBCL and I just noticed a great tip from Zach Beane for compressing the size of standalone executables. Start with rebuilding SBCL from source to add the compression option; get the source code and:

./make.sh --with-sb-thread --with-sb-core-compression
sh install.sh

When I now build my KGCreator application with:

(sb-ext:save-lisp-and-die "KGcreator" :toplevel #'kgcreator :executable t :compression t)

then the size of the standalone executable is reduced from 93MB to 19MB. I don't notice any extra startup time which is important for command line utilities.

Protecting oneself from surveillance capitalism

2020-02-10T17:30:00.000-07:00

As an author I find occasional use of Facebook and Twitter to be useful for “broadcasting” notifications of my new books, open source projects, etc. I also find gmail to be useful for some types of email.

Still, I do like to take a few easy steps to push back a little against the free use of my web behavioral data to profit corporations who I don’t do business with (and those I do):

Use ProtonMail as my primary email
Use Firefox on my Linux and macOS laptops with individual containers for Google, FaceBook, etc.
On iOS devices, favor browsing with private tabs.
Use a VPN when I am traveling and when I need to use public WiFi
Limit use of my gmail address to a backup email and as a junk email address.
For online purchases from Amazon, etc. use a secure email service that does not use the contents of your email to market to you and as data to sell to 3rd parties.
Frequently delete all cookies from web browsers that you use.
Use private browsing windows for routine use of the web
Prefer to access the web on an iOS or Chromebook - probably more secure than a laptop or PC.
Logout from web apps like Facebook, LinkedIn, Amazon, Google, Twitter, etc. to avoid active sessions.
This takes some time, but on mobile devices, Apple/Windows laptops, etc. turn off tracking on as many apps as possible.

My hopes and predictions for the next 10 years

2020-01-02T18:36:00.000-07:00

Hello everyone, I wish everyone a happy and healthy new year!

Here are my predictions for the next ten years:

Wearable devices like the Apple Watch will become widely used and because of user pushback we will see company’s like Apple, Google, Toshiba, Huawei, Samsung, etc. start to support standards that allow, for example, an Apple Watch to interact with a Samsung TV. Further, I expect a single personal device (watch or phone) to be for most users a connection hub that interacts with public kiosks, displays, input devices, etc.
Deep learning architectures and techniques will rapidly improve and will continue to rule the world, at least for a while. I expect at least one new dramatic paradigm shift for AI beyond current deep learning, reinforcement learning, etc. models.
The world economies will get hit hard and wealth will be in a larger part measured in terms of ownership of water and food production, manufacturing, technology IP, and hopefully hard assets like gold, silver, and secure cryptocurrencies. I expect most currencies will eventually be backed by about 20% gold and this will enforce some stability and help eliminate wild printing of fiat currencies. I expect SDRs, or something like them, to be the reserve currency. In ten years, I would expect SDRs to be about equally made up of US dollars, Euros, and Chinese currency.
Politically, world elites will continue to assume almost total control of news media and governments but they as a group will start to share more resources with highly trained and educated workers and allow the creation of a secure safety net that will provide a safer society because almost all people will have food and shelter needs met (this may take more than ten years). We all have to live on planet earth and everyone’s life improves with fewer wars and higher productivity.

GANs and other deep learning models for cooking recipes

2019-09-26T07:06:00.002-07:00

I retired this spring after working on artificial intelligence projects since the 1980s. Freedom from having to work on large projects for other people and companies is liberating and frees up time for thinking about new ideas. Currently I am most interested in deep learning models for generating and evaluating recipes - for now I am using a GAN model (which I am calling RecipeGAN).

When I managed a deep learning team at Capital One, I used GANs to synthesize data. During a Saturday morning quiet-time hacking sprint the first month at my new job, I had the idea to take an example program SimpleGAN that generated MINST digits and instead generate numeric spreadsheet data (using the Wisconsin Cancer Data Set that I had previously used in my books as example machine learning data). I was really surprised how well this worked: I could generate fake Wisconsin cancer data, train a classification model on the fake data, and get classification prediction accuracy on real data samples that was almost as good as a model trained on real data. This was by just making about 40 lines of code changes to the short SimpleGAN TensorFlow example/demo program. My team took this simple idea and built a robust production system around it that is well described in Austin Walter’s Medium article.

Several years ago, a fan of my CookingSpace.com web app gave me 100K public domain recipes in digital format so I should have ample training data for RecipeGAN. I will put the code and data on github when I am done with this experiment. If you are not familiar with Generative Adversarial Networks (GANs), in the cooking/recipe context the idea is simple enough: a generator model takes as input a random vector (referred to as Z vector, or latent input) and generates random recipes (for now represented as sparse vectors indicating the use of ingredients). A discriminator model learns to tell the difference between fake ingredient lists generated by the generator and real ingredient list samples. Both models are trained jointly so the generator learns to better fool the discriminator model while the discriminator model learns to not be fooled. When this process is done, the discriminator model is no longer needed. New random latent Z input vectors fed as input to the generator model hopefully generate realistic ingredient lists.

I am also interested in language generation and an end goal for my current research is to generate English directions for making the fake recipes (the ingredient lists created by the RecipeGAN generator model). This is a fun project and I also hope that the code and data will be useful to other people, even if I don’t get good results. Indeed, I am writing this blog now to encourage myself to share results no matter how well the system works. Ideas are meant to be shared.

BTW, please don’t take my proclamations of being retired too seriously. I am still helping people, as a consultant, get started on deep learning projects.

Back living in Sedona Arizona and enjoying my retirement

2019-08-09T15:24:00.001-07:00

My wife and I returned to our home in Sedona Arizona in June. I had been managing a deep learning team for Capital One in Champaign Illinois (in the research park at UIUC). I am now retired so we moved back into our house in the mountains in Central Arizona.

re: retirement: while I will might still do small interesting consulting jobs, I am retired. I am spending my time volunteering at a local food bank, hiking and kayaking with my friends, and I joined a local writers group to give myself a shove to finish a sci-fi book I have been working on for a long time.

I released a second edition to my Haskell book this week and I have edits for a new edition for my Common Lisp book that I will push to current readers soon, but I plan on no longer writing new technical books. I have written 22 technical books - probably sufficient :-)

Personally my passion is still studying artificial intelligence and deep learning but this is now research for my personal pleasure.

My large Haskell + Python project KGcreator (tool for automating the generation of Knowledge Graphs) and auto code formatting

2019-05-18T08:59:00.000-07:00

You might wonder what the topics of my large Haskell + Python project KGcreator and auto code formatting have to do with each other.

I addition to working on two Python books (Python Intelligent Systems and Deep Learning and Graph Databases), my main 'retirement' activity has been write a lot of Haskell code and a smaller amount of Python code for my KGcreator project. After reading a discussion on Hacker News yesterday about Python code tidy/auto-format tools, I decided to add Makefile targets.

After a 'stack install stylish-haskell hindent' and a 'pip install yapf', I added something like this to my Haskell top level Makefile:

tidy:
  cd src/fileutils; stylish-haskell -i *.hs; hindent *.hs
  cd src/nlp; stylish-haskell -i *.hs; hindent *.hs
  cd src/sw; stylish-haskell -i *.hs; hindent *.hs
  cd src/webclients; stylish-haskell -i *.hs; hindent *.hs
  cd test; stylish-haskell -i *.hs; hindent *.hs

And something like this to my Python top level Makefile:

tidy:
  cd botorch_bayesian_optimization; yapf *.py --style='{indent_width: 2}' -i
  cd coref_anaphora_resolution_web_service; yapf *.py --style='{indent_width: 2}' -i
  cd data_fusion; yapf *.py --style='{indent_width: 2}' -i
  cd deep_learning_keras; yapf *.py --style='{indent_width: 2}' -i
  cd deep_learning_pytorch; yapf *.py --style='{indent_width: 2}' -i
  cd discrete_optimization; yapf *.py --style='{indent_width: 2}' -i
  ....

Trivial stuff, but I already find my KGcreator and my books' codebases easier to work with. For Common Lisp and Scheme I always just rely on using the tab character to auto-indent and leave it at that. I use VSCode for both Haskell and Python development and after experimenting with a few extensions I decided it was easier to add a make target. Nothing is automated right now, I 'make tidy', 'make tests', and then 'git commit...' manually. Still to be done is adding git commit hooks. Fortunately I can use notes in one of my old blog posts as a guide :-)

I retired from Capital One yesterday

2019-03-23T06:08:00.000-07:00

With deep gratitude for a great company and a great job, I retired from my role as manager of the UIUC machine learning team and Master Software Engineer. Capital One has deep machine learning talent so check them out if you are looking for ML work.

Thanks especially to my team for being interesting to work with and for the kind going away gift of locally made Go Ban board, bowls, and Go stones. A wonderful gift. I will miss you all!

When my family and friends hear me talk about retirement they do so with great skepticism since I have retired several times already! That said, I feel like kicking back and finishing my current book project and perhaps do limited consulting work after I travel a bit to see family and friends.

Full circle from one laptop to rule them all to specialized function specific devices

2019-02-10T08:21:00.000-07:00

For about 25 years my digital life was wrapped tightly around whatever personal laptop I had. Since for most of that time I worked as a remote consultant (except for gigs at Singapore-based Ola Search, Google in Mountain View, and currently at Capital One in Urbana, Illinois) my personal laptop also covered work activities. There was something close and comforting about having one digital device that I relied on.

Digital life is very different now. Because of concerns about ‘always being online’ and not paying enough attention to the non-digital world, I favor just wearing an Apple Watch and leaving my iPhone at home. The Apple Watch is just adequate enough for phone calls, messaging, and on rare occasions email and is not anything I spend any real time paying attention to. I can spend the good part of a day shopping, walking in a park, eating out, or perusing books in a library and just spend a few minutes paying attention to my watch. A huge improvement to cellphone addiction!

For work, I have dedicated secure devices for getting my work done - the definition of purpose-specific.

For home use, I have a powerful GPU laptop from System76 that I only use for machine learning and experiments I am doing fusing ‘classic’ symbolic AI with functional components that are just wrappers for deep learning models.

Also for home use I have a MacBook that is primarily used for long writing sessions when I am working on a book project. Example code for my books tends to be short and pendantic so that development lives on the MacBook also.

I depend on my iPhone when I travel to stay organized and to have local copies of required digital assets, including on-device cached Netflix movies, Audible audio books, and Kindle books.

Lastly, the device that I spend more time on than any other (except for work devices) is my iPad on which I do close to 100% of my web browsing, almost all of my reading, enjoying entertainment, and lots of light weight writing like this blog post and editing and small additions to my current book project.

If I count all cloud-based compute infrastructure for work as one huge virtual device, the count for the digital devices I use every week weighs in at eight devices. When I retire from my job at Capital One later this spring that device count falls to five devices - still really different than the old days of having one laptop for everything.

Looking ahead to the future, perhaps only 5 or 10 years from now, I expect device profiles used by typical consumers to change a lot - mostly being one personal device that is always with you and then many different peripheral and possibly compute devices in your living and working environments that are shared with other people. I think there are three possibilities for what the one personal device may be:

A smartphone
Something like an Apple Watch
Something like a one-ear only AirPod like device

Whatever the profile is for your personal digital device, it will securely be connected to all shared devices (e.g., smart TVs, shared keyboards and monitors, shared tablets of all sizes, smart cars, home entertainment centers, the cell phone network infrastructure, point of sale devices in stores, etc.).

AWS Neptune Graph Database as a service

2019-02-05T19:02:00.002-07:00

Fascinating to see Amazon AWS supporting graph databases with their Neptune service - I have been working as a machine learning practitioner at Capital One (I manage a machine learning team there) but in a previous life, I worked with the Knowledge Graph when I was a consultant at Google and I have written a few semantic web/linked data books.

As a side project at home I have been looking into Knowledge Graph building tools so Amazon;s new offering looks useful! I like that they support both SPARQL and and Gremlin for queries.

Our humanity vs. technology and corporatism

2019-01-27T09:28:00.001-07:00

My wife and I enjoyed a performance of Sleeping Beauty by the Russian National Ballet Theater last Wednesday night at a theater on campus at UIUC. Every time I enjoy art, company of family and friends, reading a good book, cooking and enjoying a meal, etc. I appreciate being a human (i.e., a somewhat evolved great ape) and my physical and social life.

I view technology as a fairly neutral force in our lives. I judge technology on how it improves peoples' lives, health, the health of our planet, and generally how well it supports civil society. As technologists, we get value from being paid for our work and thus helping to support ourselves and our families and to spend money in our local economies (supporting local businesses and directly or indirectly hiring people working in our communities.) We also benefit from any pleasure we get learning new things while working. There are obvious bad aspects of technology and these bad aspects are mostly aligned with corporatism.

Whether or not you believe in big government or small government, I argue that an important function of government is to provide some checks and balances to corporations. Corporations by design are systems for making money for owners/shareholders. While this can positively affect society there are too many cases where things go wrong: lobbying and perverting democratic systems of government, extracting too much value out of local communities, and centralizing economic power and control - killing off smaller and potentially better rivals.

As technologists I think we can find a good balance between earning a good living to support ourselves, family, and community and also as much as possible choosing to work for organizations and projects that have a net benefit to overall society. I look at this in a way that is analogous to a "carbon tax" for technologies we create and use. Let's call it a "technology value tax" where we try to at least make our work "carbon neutral."