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Frank Buna

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Every human bliss and kindness, every suspicion, cruelty, and torment ultimately comes from the whirring 3-pound “enchanted loom” that is our brain and its other side, the cloud of knowing that is our mind. It’s an odd coincidence that serious study of the mind and the brain bloomed in the late 20th century when we also started to make machines that had some mind-like qualities. Now, with information technology we have applied an untested amplifier to our minds, and cranked it up to eleven, running it around the clock, year after year. Because we have become a culture of crisis, we are good at asking, what has gone wrong? What could go wrong? But is the conjunction of natural and artificial mind only ill-favored, or might we not learn from both by comparison? A learning opportunity. Years ago I decided to study animal behavior, hoping that it would help us to better understand our nature. But the culture then was indifferent and sometimes hostile to the idea of human nature having anything to do with biology. The enlightened “narrative” was radically tilted towards nurture, not nature. Once my monkey studies were published, facing society’s antipathy, I had to become some other cog in the Great Grinding Wheel. Long afterward, I looked around and found that (a) in the interim much had been learned about the mind, and (b) our species was still fanning a social, political, economic, environmental dumpster-fire. True to my previously thwarted nature, I started looking for some truth in the revived mind sciences. Philosopher Thomas Metzinger publishes , which says that consciousness is constructed by the brain like a virtual reality. 2009: The Ego Tunnel But what truths would be helpful? Would it do us any good to know how our minds arise from matter? How our burrow through time and space to create our own reality? How that same ego is the tail wagged by the ? Who will care, given our multiple unfolding crises? ego tunnels dog of the unconscious Some think that our minds set a distant, unreachable boundary on what machines could ever do. Others think that machines could someday surpass us like we surpass a bug. These extremes and many views in between offer ways for us to explore the nature of minds, what they can do, and what they might achieve. The Nobel Foundation establishes a new Mind Science prize amid cries of ‘wrong-headed’, ‘unprecedented’, and ‘long overdue’. 2038: Some areas of research connecting minds and machines are weighty indeed but obscured from public attention. Values and decision theory. Our ongoing attempts to make artificial minds are starting to teach us about ourselves. When we give machines the ability to make judgments we run into now-familiar moral dilemmas. The self-driving car as executioner. Pernicious biases. From a higher-level view, AI theorists see the above as questions about values. Before they get too powerful, we need our intelligent machine agents to act as if they had values like ours. Research currently calls the , meaning ‘aligned with our values.’ problem, AI alignment Alignment researchers prefer to work with formal, math-like systems, and a favorite is a framework called decision theory. It concerns how to make decisions about reaching goals when you’re assuming a set of values. The values are in a utility function that rates the preferences for all possible outcomes of the decisions. Decision theories let researchers tackle general questions like: how do you formalize values so that pursuing one value doesn’t violate another? So far, decision theory shows how hard it can be to use values to guide even the simplest behavior for a powerful agent. Suppose you tell the AI to cure cancer, but not to cause any other side effects in the world. It might conclude that “no side effects” means continuing to let people die of their cancer. Instead of stopping after filling the bucket the sorcerer’s apprentice the castle or even the whole world. might indeed flood Big Spill. image of Victoria Falls, source: otsuka88 @ Pixabay Humans have developed ethical systems without gaining any consensus. Ethicist William Frankena made the following list of things that many people value, nearly all of which can conflict with each other. Adapted from Arbital How do we resolve the fact that human values are incomplete, contradictory, and can change with circumstances? 2004: Eliezer Yudkowsky defines ideal human values to be our “Coherent Extrapolated Volition” Some have proposed having the machines themselves help us discover more coherent, long-term values. Others have found that the , at least for machines, is fiendishly complex. They have also found that, if you somehow can specify the values, it is hard to see how to make a machine retain them, especially if it is smart enough to be capable of modifying itself. value learning problem Consciousness and self-modeling. Researchers are trying to devise how to make machines that behave adaptively. The crux is that to improve how to do some task you need to model the situation in which it occurs and then use the model to evaluate (decision theory again) different approaches. For a machine operating in the real world, the situation includes the machine itself. So a flexibly adaptive machine must model its very self as part of its operating model. “Consciousness stems from the structure of the self-models that intelligent systems use to reason about themselves.” 2007: — Drew McDermott , AI researcher But wait — there’s a strong theory that human beings , and that our mental models of ourselves-in-the-world the experience of consciousness. So will we, intentionally or not, make machines that are phenomenally conscious? Would their conscious experience resemble ours in some ways (could they suffer? be happy? trustworthy? devious?) or be completely alien? Would we believe what they said to us about their internal conscious states? do the same thing cause The really hard problem of consciousness is the problem of [having] experience.” 1995: “ — David Chalmers We all know that living a good life — and for many of us, living any life — is fraught with difficulty. We are getting a fresh look at that bedrock fact. This happens when we try to imagine safely getting help from our intelligent machines in realistic settings: where they are choice-making agents who have to model their world and themselves embedded in it. The theorists call the issue “embedded agency.” Embedded agency. source: Scott Garrabrant This is Emmy. Emmy is playing real life … Emmy is within the environment that she is trying to optimize.” 2018: “ Garrabrant, Embedded Agents To decide on future courses of action in a changing world, you have to predict what new situations will arise to which you must respond. But your own previous behavior will affect what those new situations will be. And likewise for your behavior before those previous situations, and so on, all the way back to Start. The more you chain together these predictions over time, the less accurate they will be. It also gets rapidly more difficult to compute longer chains of actions. This prediction issue alone might paralyze a literal-minded AI. In the same situation, people just “go for it.” We make a decision, telling ourselves we’ll . We certainly don’t want a powerful machine doing that. work it out somehow Furthermore, if you are an agent who makes choices in the world, then when you see the outcome of a choice you also know that you could have done otherwise. In people, this can cause feelings of justification ( ) or regret ( ). These feelings help integrate us into our society, and so one theory (Frith & Metzinger, ) is that we evolved these feelings to get along with others. I made the right choice oh, if only I hadn’t done that What's the Use of Consciousness? Any problem-solving machine should also respond to feedback on its choices in ways that improve future ones. The first thing it should do is what we can do but usually don’t: adjust our world model based on what happened with our choice. We could also think harder about the next decision. For “big” decisions we often do that. But research says that most of our decisions are made unconsciously, and our conscious thinking merely justifies them. A machine could improve a decision by thinking more, but there are three issues, which we can call the think-more problems. One is the chaining prediction issue mentioned above. Another is knowing it has thought enough before taking action. The third is logical twists that can arise when trying to predict its own behavior in order to plan that same behavior. when A very advanced machine might decide to push this farther by trying to improve its basic intelligence. That idea leads to a paradox. For a successful improvement, it would need to predict what its smarter self would do. But if it could predict that, then it would have to already be the smarter self! Both the think-more and improve-me problems are similar from a computer science point of view, and researchers are trying to figure them out. Again, faced with the issue of possible and unknowable improvement, people eventually decide to just do or not do. We can’t trust an AI with that decision. The problem just described is named Vingean Reflection in honor of sci-fi author Vernor Vinge, who said that a writer is unable to predict the behavior of a character smarter than the writer. Other versions are: a child can’t write (or reason) realistically about an adult; humans can’t predict what a superintelligent AI would do. We know that people can find it hard to trust their experts, because of Vinge’s reason. If we find a way to think usefully about AIs smarter than us, maybe it would apply to our own trust issues as well. Agency and mentalizing. Our minds are primed to explain events as if they were the result of intentions. We have our own intentions, we believe that other people have theirs, and we even assign intentions to random natural events. To apply such a mentalizing analysis to another person is called using a Theory of Mind. Our predictions are weak, but we use them to muddle through. An intelligent machine interacting with humans would need a theory of others’ minds as part of its world model. But would an AI model a human differently than it would model a car, or a tornado? You could say that, sure, a human has goals, so the AI tries to infer the goals and use that to predict behavior. Maybe we would learn something about our own goals and behavior if a really smart AI modeled how we use them. And that’s an encouraging idea. First, consider how an AI might model things that aren’t alive. The AI could say that the car has goals, which are quite simple: when the piston goes down, the goal is to draw in an optimal fuel/air mixture into the cylinder. So the fuel injector implements that goal, given input from various places, like current road speed and accelerator position. When the brake pedal is depressed, the goal is to stop smoothly. In the case of a tornado, the “goals” would be things like absorbing and dispersing heat energy from the air. Or, conservation of angular momentum. And maybe transfer of energy and momentum to whatever stands in the tornado’s way, like buildings and trees. So, what makes people different from “things” like cars and tornados? In order to explain how an information processing system can have a model of something, there must be a prior notion of intentionality, that explains why and how symbols inside the system can refer to things. 2007: — Drew McDermott The pursuit of goals is evidence that we have intentions. But, to current philosophers and psychologists (and, originally, Franz Brentano) “intentionality” means that all our mental states are : either other mental states or things in the outside world. In our mentalizing, we imagine elaborate “ ”. Such as (count the italicized verbs to get the level): “Sue that Ralph that Alan his book club, but Alan doesn’t even books.” directed towards something levels of intentionality believes thinks wants to join like A lot of philosophy and science has been written about the difference between animate and inanimate things, or things that have agency and intentionality versus those that don’t. We might clear some of that up if we build an AI that models intentionality correctly. Another implication of such modeling takes us back to the consciousness issue. Maybe an AI that successfully models the intentions of human agents would do like we do. It would apply the same modeling principles to itself and wind up being, or at least . Hence an ethical can o’ worms: how do we treat the AI, and how would it treat us in return? acting like it was, conscious Overturning Stones. Our attempts to understand what it would take to have aligned AIs are also a fresh look at some issues of being human. Old problems of philosophy and psychology are being approached using new tools such as decision theory, game theory, logic, and probability. The AI researchers who study decision theory know that the extreme rationality of their theorizing could seem cold and alien, as well as hard to follow. They also know that our behavior is riddled with unconscious cognitive biases, learned prejudices, and narrow ideologies. We don’t want our lousy thinking skills transferred to our machines. When we research how they can do a better job, we might improve ourselves. First melding of multiple humans into a multi-mind. Was by an obscure informaticist. 2049: predicted in 2020 Even if advanced AI never happens the effort might be helpful with human problems. Nick Bostrom and others have noted the parallel between advanced AIs and human organizations like governments and corporations. Both exceed the capabilities of individual humans. Both tend towards the amoral. We need a better consensus on the values for aligning them. Then we need ways to sustain that alignment, so we can trust them when their successors gain new powers.

What We Should Learn from the Tension Between Mind and Machine

Delta Sharing is an independent open source project that makes it easy to share massive amounts of live data with other organizations openly and securely. This article describes a smoke test of the new Docker-based open-source Delta Sharing server. Introduction Delta Sharing works across vendors, system boundaries, and clouds. It’s and . open format open source Any or client can read the data. Many commercial clients support it too. Delta Sharing is a simple yet powerful and elegant way of sharing your data. And without anything proprietary, it is naturally multi-cloud. pandas Apache Spark Delta Sharing Linux Foundation At the time of writing this, was released. version 0.2.0 of Delta Sharing My favorite feature of this release is the new Docker image of the sharing server. The Docker image gives you more flexibility and options for running the server. Delta Sharing using Docker Delta Sharing Docker Image Let’s look at the Docker image first, published at . Assuming you have Docker installed, you can start Delta Sharing with the following command: Docker hub docker run -p 9999:9999 \
--mount type=bind,source=/home/ec2-user/config.yaml,target=/config/delta-sharing-server-config.yaml \
deltaio/delta-sharing-server:0.2.0 -- \
--config /config/delta-sharing-server-config.yaml Docker then retrieves the image if necessary and runs a container. The parameter maps the Delta Sharing server port to the same Port on my local machine; I am using 9999. The makes the from my local machine visible inside the container. See the discussion below about how the Delta Sharing server can get access to the cloud object storage. -p --mount config.yaml Configuration config.yaml The file specifies the port and endpoint the sharing server is listening to, whether it uses a bearer token, etc. config.yaml 9999 /delta-sharing The most important configurations are the shares, schemas, and tables offered by the sharing server from your . Delta Sharing follows the Lakehouse lake first approach. With Delta Lake, you can share data from your existing data lake as Delta tables. For this article, I decided to export car data stored in an S3 bucket with the name: . deltafm2805 # The format version of this config file
version: 1
# Config shares/schemas/tables to share
shares:
- name: "share1"
  schemas:
  - name: "default"
    tables:
    - name: "cars"
      location: "s3a://deltafm2805/cars"
 

#authorization:
#  bearerToken: “1234”

host: "localhost"
port: 9999
endpoint: "/delta-sharing" S3 Storage To check the data on the S3 bucket, you can use the AWS CLI and list the bucket . Note that I have other data that is not shared in my case because it is not enabled in the config.yaml deltafm2805 $ aws s3 ls deltafm2805
                           PRE cars/
                           PRE range/ Authorization The Delta Sharing server requires authorization to access S3. There are various ways to enable this. Technically, you could pass the AWS key ID and the secret key as an environment variable using with . -e docker run docker run -p 9999:9999 \
-e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID  \
-e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY  \
... I don’t recommend doing this since everyone on the host machine with the privilege to run a simple command will be able to see your AWS credentials. ps A better way is to pass the credentials using the user-data on EC2. metadata service Myself, I recommend creating an EC2 in IAM that grants EC2 access to S3. Then, assign the role the instance as shown in the screenshot below. instance profile This approach eliminates the need to put the keys on the instance or into its user-data configuration. Wherever there are no keys, they cannot be compromised. Verify it Works! Profile File To verify that Delta Sharing works as expected, retrieve a shared car data set and display a filtered subset. The code is using the profile file, which defines the endpoint and bearer token of the server. The content of that JSON file looks as follows: aws.share {
 "shareCredentialsVersion": 1,
 "endpoint": "http://localhost:9999/delta-sharing",
 "bearerToken": "" 
} Python Receiver Client In the receiver code, create a client and use it to read the data as a pandas data frame with . delta_sharing load_as_pandas import delta_sharing

# Point to the profile file. 
profile_file = "aws.share"

# Create a SharingClient.
client = delta_sharing.SharingClient(profile_file)

# List all shared tables.
print(client.list_all_tables())

# load data as pandas dataframe (or Spark if you prefer that)
table_url = profile_file + "#share1.default.cars"
cars = delta_sharing.load_as_pandas(table_url) Then use pandas to filter the data to retrieve all Volkswagen cars: cars[cars['car_make']=="Volkswagen"] In my case, I have two Volkswagen in the data set, and the result looks as follows: Under the Hood Pre-signed URLs Delta Sharing is using pre-signed, short-lived URLs; therefore, data is retrieved at the speed of the cloud object storage. Throughput and bandwidth are not limited by the sharing server. Delta Sharing REST API The example above used pandas after installing the necessary library with . The pandas library is implemented on top of the Delta sharing REST API documented at . pip GitHub If you want to explore accessing the API directly, you could, e.g., use the following command to get all tables in and the schema: share1 default curl -v   -H 'Accept: application/json'  'http://localhost:9999/delta-sharing/shares/share1/schemas/default/tables' The output then looks as follows: {"items":[{"name":"cars","schema":"default","share":"share1"} Unless you want to build your own integration with Delta Sharing, using the REST API directly won’t be necessary for most users. Multiple Delta Lakes A single Delta Sharing server can be set up to serve data from various Delta Lakes. Make sure to list them in the profile file and grant access to all Delta Lakes from the EC2 instance profile IAM role explained above. Other Options and Conclusion At the moment, there are three main options to deploy Delta Sharing: Host your own Delta Sharing server, e.g., on your laptop or a cloud instance. If you want to get started but feel uncomfortable with Docker, I’d recommend this way. The Delta Sharing server itself can be . downloaded Run your sharing server using Docker, as explained in this article. Actually, this isn’t more difficult than running the server without Docker. Using Docker, you will always run the newest image of the sharing server. So I recommend this option for running your own server. [If you don’t feel comfortable with Docker, ask a good friend to show you the basics and take a self-paced training course]. Share data using Delta Sharing from a . This is by far the easiest solution because it eliminates “the heavy lifting.” Databricks notebook Delta Sharing is built into your Databricks workspace, so you don’t need to worry about installing, configuring, operating, patching, and monitoring the sharing server. Using a Databricks notebook, everything can be done with simple SQL statements. You create a share, add a table to a share, create a recipient and finally grant the recipient access to that share (see diagram below for the SQL statements). More? H ere is the at Data and AI Summit from Matei. video of the Delta Sharing announcement Delta Sharing with Google Colab (client perspective), GeekCulture article Delta Sharing on GitHub Slice & DAIS, a , also available in . summary of everything announced at Data and AI Summit Spanish Please like this article and share it on social media if you enjoyed reading it. For more cloud-based data science, data engineering, and AI/ML follow me on (or ). Twitter LinkedIn

Share Large Amounts of Live Data With Delta Sharing and Docker

Highlights from the Data and AI Summit (former Apache Spark Summit) for the Busy IT Professional Right now, I am actually waiting for the keynote of day 2 to kick off. So let me summarize the announcements from day 1 of the Data and AI Summit (DAIS) for you. Delta.io Delta Lake has grown up :-). The Linux Foundation-owned, open-source project that brings reliability to your data lake matured to version 1.0 now. Happy Birthday, Delta! Key features of Delta 1.0: Delta.io , JVM implementation that understands tx protocol, but does not need SparkRust implementation for Delta 1.0, also Support for Ruby and Go. Delta Standalone Reader Delta 1.0 supports Spark 3.1 - also improves predicate pushdown. Delta Sharing A brand new open source project for data sharing was announced at DAIS. With you can share massive, live data from your Lakehouse between clouds or on-premises. It’s secure, fast, cheap, and reliable using underlying cloud storage systems such as S3, ADLS, and GCS. And it’s open source & open standard. Recipients accessing shared data can connect to it through pandas, Tableau, or dozens of other systems that implement the open protocol. . Delta Sharing directly #DataAISummit Unity Catalog Databricks rolled out a data catalog, . Unity Catalog solves a major issue. Imagine a CSV file stored in your S3 data lake and you want to grant access to certain rows only? Unity Catalog Unity Catalog enforces permissions at the row, column, or view level instead of the file level. It governs tables and ML models. Simply use ANSI SQL standard GRANT statements, or discover data assets from the UI. Works for the Lakehouse on all clouds. Delta Live Tables My fav announcement: Delta Live Tables. Data flow made simple: Specify the outcomes that a pipeline needs to achieve using SQL or Python. Treat your transformations and data quality expectations as code. #DataAISummit And now for day 2 of DAIS! Databricks Machine Learning Databricks Machine Learning brings together managed , and introduces new components, such as AutoML and the Feature Store, and supports the full ML lifecycle. MLflow Feature Store MLflow integration enables the to package up feature lookup logic hermetically with the model artifact. When an MLflow model that was trained on data from the Feature Store is deployed, the model itself will look up features from the appropriate online store. Feature Store The Databricks Feature Store automatically tracks the data sources used for feature computation, as well as the exact version of the code that was used. AutoML allows you to quickly build and deploy machine learning models by automating the heavy lifting of , and AutoML detects the best preprocessing, ML model, and hyperparameters for you and creates a notebook with all steps required. It automatically tracks trial run metrics and parameters with MLflow and easily enables teams to register and version control their models in the for deployment. You define the max runtime that you want to spend to solve the task. AutoML preprocessing feature engineering, model training/tuning. Databricks Model Registry There are more video resources from DAIS 2021 that I recommend: The , , , , , and . fireside chat with Bill Inmon (DWH inventor) Delta 1.0 announcement Delta Sharing SQL and Photon updates Unity Catalog Delta Live Tables Slice & DAIS 2021 — EMEA Live Event Join us for the first that talks about all the new announcements in a beginner-friendly way. session Slice & DAIS Previously read behind a paywall at https://medium.com/geekculture/what-is-new-in-data-and-ai-2021-376cefe67fb3

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