My wrap up of interesting/practical machine learning of 2020

As part of my work (but also personal interest) I try to stay on top of not just the research side of machine learning (ML — what many folks think of as part of artificial intelligence) but also practical and interesting examples. On the professional side, this is because a lot of the software I’ve worked to create over the past 13 years has directly built in elements of machine learning. On a personal level, I’m interested particularly in the economic impacts of some of what machine learning will bring: which jobs will be automated away or significant portions will be automated (see: “Humans Need Not Apply”).

To some extent, this is a sort of dystopian view, and I won’t get into my thoughts on what can/should be done about it, but I do want to point out that it’s not just the simple, repetitive, or labor-intensive jobs that can be automated. Some of the most interesting developments in machine learning over the past couple years have been in creative tasks and tasks which most people associated with the type of thinking only a human could do.

In this blog, I’m going to outline some of the most interesting projects in ML/AI that fit the bill of doing creative tasks or logical reasoning and which have online demos or videos of the demos, most of which have launched roughly in the past 1-2 years. You can actually go play with many of these things yourself to get a sense of where certain aspects of ML are at the start of 2021.

Image Generation

One of the things that most people associate with “a thing only a human could do” is to generate art. Maybe it’s taking a lovely photo or painting something very creative. Here are some online demos that show that machines can now do this too (and how well they do so):

DeepArt allows you to upload a photo that you take and then apply a style. For example, here I am “painted” automatically by a machine in the style of Vincent Van Gogh in just minutes from a photo I took in seconds. There are a number of interesting implications to this, ranging from forgery to novel artwork creations to “allowing anyone to become an artist.”

GauGAN allows you to create photo-realistic images by just drawing an image like you would in MS Paint. Here’s an image I drew of a mountain and a hill in the ocean next to a beach with a cloud in just a few minutes and an example output:

It doesn’t take that much to imagine how you could use something like to create art of places that don’t/can’t exist and you can imagine combining strategies of something like this with something like DeepArt to create paintings that require very little skill and only a good imagination.

Dall-E: Taking the previous examples a step further, what if you could just type up what you want an image of? That’s what Dall-E does (fresh off the presses as of January 5, 2021). Dall-E can take text that you type and generate an image for it. Their examples on the blog do a lot to spark imagination and you can play around with a few examples. You can go to this link to see how something like this might generate an image of “an armchair in the shape of an avocado” or “a professional high quality emoji of a happy alpaca” or my favorite: “an illustration of a baby daikon radish in a tutu walking a dog.” This type of thing has the potential to radically change illustration and design work.

Audio/Music Generation

It’s not just visual art/artists that are going to be under the ML gun. ML can now make music too.

MuseNet allows computers to dynamically generate new music from a text prompt. For example, “use the first 5 notes of Chopin Op 1, No 9 as a basis to generate an entirely new song.”

The original piece
Computer generated piece

If you follow through to the MuseNet blog, you’ll see it can combine musical styles, generate new music from a few starting notes, or just give it a prompt like “Bluegrass piano-guitar-bass-drums.”

GPT-3 lyric generation. It doesn’t just stop at the tone generation, ML can generate lyrics now too. Here’s a song with lyrics written entirely by a machine:

Oh yeah, and ML can even sing your song for you. Here’s over 7000 songs that are generated/sung entirely by machines. Are they perfect? No — especially not rhyme scheme or some of the voice impersonations. But those are getting better too…


There are now a series of “this _____ does not exist” generators that you can explore. This person doesn’t exist, this cat doesn’t exist, this horse doesn’t exist, this artwork doesn’t exist, and hey, even this chemical doesn’t exist because why not. Reload each page to see a new one of these that don’t exist. Don’t find something category of thing you want to create? There’s a way to generate the new category here if you have some software knowhow. These seem fairly benign at the surface (who cares that a fake person/cat/horse/… image could be generated), but the implications to this type of thing go far beyond the amusing.

Want to impersonate another person’s voice? Generate your own audio as Dr Who or HAL 9000 at

Want to impersonate another person entirely as a video? All of the following are fabricated by having ML figure out how to generate a person’s lookalike with explicit facial expressions.

This is over 2 year old technology
Now it’s coming up in an entirely new way to generate satire and much more nefarious purposes
Definitely not the most PC, but that’s part of the craziness of ML-generated audio/video

The visual artifacts you see on these videos are going to disappear over time as computational power increases. Now imagine combining these 2 together: fake speech generated by a ML model of a famous person combined with a fake video of that person’s facial expressions and movements and you can see you don’t even need to hire a voice actor to create really serious challenges in categories of fake news, legal challenges against verbal contracts, etc.


The classic example of ML beating a human lies in the realm of Chess, and more recently with a game computers were thought to be unable to play competitively, Go. But there are other games you may not think of.

ML can play pictionary, for example now live in your browser against you. Or hey, need help drawing your pictionary item? ML can help you complete your sketch. It can answer trivia questions. Or it can make up a dungeons and dragons game on the fly for you. Or check out these 3 videos of ML playing games you’ve probably played — and doing so better than you.

ML playing Mario
ML playing pool
My favorite: AI playing hide and seek

There are a number of interesting implications to this type of thing. One is that — if you play games — I imagine we’ll see much more complicated AI bots that play against you. But the AI playing hide and seek in particular is interesting because it involves some lightweight construction with specific goals. There are far more advanced versions of engineering and behavioral optimizations that exist outside of these demos. For example, in the past year, an AI pilot beat the top Air Force fighter pilots 5-0 in a dogfight simulation. You can see where “games” can quickly apply to real-world situations.

Other Professions

There are already entire companies set up to reduce time, improve the quality of output, or entirely replace people from the process of certain professions. Here are a few recent examples:

And there are a variety of other professions which already have working demos or systems in place to help.

This is not comprehensive and “academic” and certain other types of applications that are still too new to be available to the public in demo form aren’t here but I hope this helps show a bit of what’s come around in the past year or so in the world of ML in ways you can go exploring yourself!

Antenna Design Genetic Algorithm


For certain classes of antennas, e.g. Yagi-Uda antennas, the design characteristics have no known “best case” numeric values.  That is, if you want to design a Yagi-Uda antenna for a particular frequency, there is no known numeric solution for the width of the dipoles, number of dipoles, and distance between each dipole in order to achieve the highest gain.  Instead, people rely on experimental evidence: the designs of a number of common frequencies have been tested in the field to produce certain amount of gain, so if you know what frequency you’re looking to design for, you go look up the tables based upon what others have tested for.  If you’re looking for an entirely unique frequency, you have to go experiment yourself.

New Approach

I wrote a MatLab program to use a genetic algorithm to modify the parameters of a antenna and eventually “give birth” to a “best known case” antenna based upon forward gain, etc. Currently, it uses NEC2 (Numerical Electromagnetics Code 2) as the processing engine. It writes out a text file to disk, then calls NEC2 to process that file.  This allows us to try a number of unknown antenna designs and permute possible solutions.  It can be run in a distributed fashion, with each machine “phoning home” to a central database which then redistributes the best-known designs to the worker machines.

The output is something like the following, which shows the forward gain of a given design through a set of frequencies

Output from a forward gain analysis


Genetic Algorithm:

function [beta,stopcode]=ga(funstr,parspace,options,p1,p2,p3,p4,p5,p6,p7,p8,p9)
% Genetic Algorithm for function maximization.
%  beta       = (1 x K) parameter vector maximizing funstr
%  stopcode   = code for terminating condition
%                == 1 if terminated normally
%                == 2 if maximum number of iterations exceeded
%  funstr     = name of function to be maximized (string).
%  parspace   = (2 x K) matrix is [min; max] of parameter space dimensions
%               or, if (3 x K), then bottom row is a good starting value
%  options    = vector of option settings
%  p1,p2,...,p9 are optional parameters to be passed to funstr
% where:
% options(1) = m (size of generation, must be even integer)
% options(2) = eta (crossover rate in (0,1); use Booker's VCO if < 0)
% options(3) = gamma (mutation rate in (0,1))
% options(4) = printcnt (print status once every printcnt iterations)
%                Set printcnt to zero to suppress printout.
% options(5) = maxiter (maximum number of iterations)
% options(6) = stopiter (minimum number of gains < epsln before stop)
% options(7) = epsln (smallest gain worth recognizing)
% options(8) = rplcbest (every rplcbest iterations, insert best-so-far)
% options(9) = 1 if function is vectorized (i.e., if the function
%                can simultaneously evaluate many parameter vectors).
%    Default option settings: [20,-1,0.12,10,20000,2000,1e-4,50,0]
% Note: 
%    The function is maximized with respect to its first parameter,
%    which is expressed as a row vector.
%    Example: 
%      Say we want to maximize function f with respect to vector p,
%      and need also to pass to f data matrices x,y,z.  Then,
%      write the function f so it is called as f(p,x,y,z).  GA will
%      assume that p is a row vector.

months = ['Jan';'Feb';'Mar';'Apr';'May';'Jun';...

if nargin>2
   if isempty(options)
m=options(1); eta=options(2); gam=options(3);
stopiter=options(6); epsln=options(7);

% Use Booker's VCO if eta==-1
vco=(eta<0);  eta=abs(eta);

% Cancel rplcbest if <=0
if rplcbest<=0, rplcbest=maxiter+1; end


% Draw initial Generation
if b0rows>0
  parspace=parspace([1 2],:);

% Initial 'best' holders
bestfun=-Inf; beta=zeros(1,K);

% Score for each of m vectors

% Setup function string for evaluations
evalstr = [funstr,'(G'];
if ~vecfun
        evalstr=[evalstr, '(i,:)'];
if nargin>3, evalstr=[evalstr,paramstr(1:3*(nargin-3))]; end
evalstr = [evalstr, ')'];

% Print header
if printcnt>0
   disp(['Maximization of function ',funstr])
   disp('i      = Current generation')
   disp('best_i = Best function value in generation i')
   disp('best   = Best function value so far')
   disp('miss   = Number of generations since last hit')
   disp('psi    = Proportion of unique genomes in generation')
   disp(sprintf(['\n',blanks(20),'i     best_i        best     miss   psi']))

iter=0;  stopcode=0;
oldpsi=1;  % for VCO option
while stopcode==0
   % Call function for each vector in G
   if vecfun
     for i=1:m
   bf=max([bf0 bestfun]);
   if fgain>epsln
   if fgain>0
   if printcnt>0 & rem(iter,printcnt)==1
        ckhr=int2str(ck(4)+100);  ckday=int2str(ck(3)+100);
        ckmin=int2str(ck(5)+100); cksec=int2str(ck(6)+100);
        timestamp=[ckday(2:3),months(ck(2),:),' ',...
           ckhr(2:3),':',ckmin(2:3),':',cksec(2:3),' '];
        disp([timestamp,sprintf('%6.0f %8.5e %8.5e %5.0f %5.3f',...
                [iter bf0 bestfun inarow psi])])
        save gabest beta timestamp iter funstr
   % Reproduction
   r=rand(1,m); r=sum(r(ones(m,1),:)>pcum(:,ones(1,m)))+1;
   % Crossover
   if vco
        eta=max([0.2 min([1,eta-psi+oldpsi])]);
   if y>0
     % choose crossover point
     for i=1:y
   % Mutation
   % Once every rplcbest iterations, re-insert best beta
   if rem(iter,rplcbest)==0

if printcnt>0
   if stopcode==1
        disp(sprintf('GA: No improvement in %5.0f generations.\n',stopiter))
        disp(sprintf('GA: Maximum number of iterations exceeded.\n'))
% end of GA.M
function [gain_t]=Yagi(p)
% This program is used with a getic optimization code.
% It creates an NEC input file given the parameters for a 3 element YAGI
% antenna.  Then, it runs NEC and reads in the parameters(Gain, Impedance, etc) generated by NEC.
%=========Create NEC input file========================================
D=0.0085; % diamter of elements in wavelengths
Lr=p(1); %perimeter of reflector
Ls=p(2); %perimeter of driven element
Ld=p(3); %perimeter of director
Sr=-p(4); %location of reflector
Sd=p(5);  %location of director
%    Geometry input for NEC

rsl = Lr / 4; %reflector single side length: square loop, one side = permiter / 4
ssl = Ls / 4; %driven single side length: square loop, one side = permiter / 4
dsl = Lr / 4; %director single side length: square loop, one side = permiter / 4
num_segments_per_side = 7;

fprintf(FID_nec,strcat('CM UDA-YAGI SQUARE LOOP ANTENNA','\n'));
fprintf(FID_nec,strcat('CE FIle Generated by MatLab','\n'));
fprintf(FID_nec,'GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\n',1,num_segments_per_side,Sr,0,-Lr/2,Sr,0,Lr/2,R); %Reflector
fprintf(FID_nec,'GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\n',2,num_segments_per_side,0 ,0,-Ls/2, 0,0,Ls/2,R); %Driven Element
fprintf(FID_nec,'GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\n',3,num_segments_per_side,Sd,0,-Ld/2,Sd,0,Ld/2,R); %Director
%    Program Control Commands for NEC
fprintf(FID_nec,'EX %3i %3i %3i %3i %8.4f %8.4f\n',0,2,4,0,1,0);%Exitation Command wire 2 segment 4
fprintf(FID_nec,'FR %3i %3i %3i %3i %8.4f %8.4f\n',0,1,0,0,2400,0);%set freq to 299.8 MHz so wavelength will be 1m
fprintf(FID_nec,'RP %3i %3i %3i %3i %8.4f %8.4f %8.4f %8.4f\n',0,1,1,1000,90,0,0,0);%calculate gain at boresite
%=======Create file to pipe to NEC ===================================
%=======Run NEC======================================================
!NEC2Dx500 < input_CMD >tmp;
%=======Read Data form NEC output file===============================
[freq,Z,gain_t,E_theta,E_phi,n_freq_meas,run_time] = nec_read('NEC.out');