Azizi Othman

We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets
worse
and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs.We unify the above phenomena by defining a new complexity measure we callthe
effective model complexity
and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually
hurts
test performance

We demonstrate that models trained only in simulation can be used to solve a manipulation problemof unprecedented complexity on a real robot. This is made possible by two key components: a novel
algorithm, which we call automatic domain randomization (ADR) and a robot platform built formachine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibitvastly improved sim2real transfer. For control policies, memory-augmented models trained on anADR-generated distribution of environments show clear signs of emergent meta-learning at testtime. The combination of ADR with our custom robot platform allows us to solve a Rubik’s cubewith a humanoid robot hand, which involves both control and state estimation problems

Energy based models (EBMs) are appealing due to their generality and simplicity
in likelihood modeling, but have been traditionally difficult to train. We presenttechniques to scale MCMC based EBM training on continuous neural networks,
and we show its success on the high-dimensional data domains of ImageNet32x32,
ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better
samples than other likelihood models and nearing the performance of contemporary
GAN approaches, while covering all modes of the data. We highlight some unique
capabilities of implicit generation such as compositionality and corrupt image
reconstructionandinpainting. Finally, weshowthat EBMs areuseful models across
a wide variety of tasks, achieving state-of-the-art out-of-distribution classification,
adversarially robust classification, state-of-the-art continual online class learning,
and coherent long term predicted trajectory rollout

Reward learning enables the application of rein-
forcement learning (RL) to tasks where reward isdefined by human judgment, building a model of
reward by asking humans questions. Most work on reward learning has used simulated environ-
ments, butcomplexinformationaboutvaluesisof-
ten expressed in natural language, and we believe
reward learning for language is a key to making
RL practical and safe for real-world tasks. In this
paper, we build on advances in generative pretrain-
ing of language models to apply reward learning
to four natural language tasks: continuing textwith positive sentiment or physically descriptive
language, and summarization tasks on the TL;DR
and CNN/Daily Mail datasets. For stylistic con-
tinuation we achieve good results with only 5,000
comparisons evaluated by humans. For summa-
rization, models trained with 60,000 comparisons
copy whole sentences from the input but skip irrel-
evant preamble; this leads to reasonable ROUGE
scores and very good performance according to
our human labelers, but may be exploiting the fact
that labelers rely on simple heuristics.

The emergence of complex life on Earth is of-
ten attributed to the arms race that ensued from a
huge number of organisms all competing for finite
resources. We present an artificial intelligence re-search environment, inspired by the human gamegenre of MMORPGs (Massively Multiplayer On-
line Role-Playing Games, a.k.a. MMOs), thataims to simulate this setting in microcosm. As
with MMORPGs and the real world alike, our en-
vironment is persistent and supports a large andvariable number of agents. Our environment is
well suited to the study of large-scale multiagent
interaction: it requires that agents learn robustcombat and navigation policies in the presenceof large populations attempting to do the same.Baseline experiments reveal that population sizemagnifies and incentivizes the development of skillful behaviors and results in agents that out-compete agents trained in smaller populations.We further show that the policies of agents with
unshared weights naturally diverge to fill different
niches in order to avoid competition

Most existing adversarial defenses only measure robustness to
L
p
adversarialattacks. Not only are adversaries unlikely to exclusively create small
L
p
perturbations, adversaries are unlikely to remain fixed. Adversaries adapt andevolve their attacks; hence adversarial defenses must be robust to a broad rangeof
unforeseen attacks
. We address this discrepancy between research and realityby proposing a new evaluation framework called
ImageNet
-
UA
. Our framework
enables the research community to test ImageNet model robustness against attacks
not encountered during training. To create
ImageNet
-
UA
’s diverse attack suite,we introduce a total of four novel adversarial attacks. We also demonstrate that,in comparison to
ImageNet
-
UA
, prevailing
L
∞
robustness assessments give anarrow account of adversarial robustness. By evaluating current defenses with
ImageNet
-
UA
, we find they provide little robustness to unforeseen attacks. Wehope the greater variety and realism of
ImageNet
-
UA
enables development of
more robust defenses which can generalize beyond attacks seen during training

We study the transfer of adversarial robustness of deep neural networks between different perturba-tion types. While most work on adversarial exam-
ples has focused on
L
∞
and
L
2
-bounded pertur-
bations, these do not capture all types of perturba-tions available to an adversary. The present work
evaluates 32 attacks of 5 different types againstmodels adversarially trained on a 100-class sub-set of ImageNet. Our empirical results suggestthat evaluating on a wide range of perturbationsizes is necessary to understand whether adver-sarial robustness transfers between perturbationtypes. We further demonstrate that robustness
against one perturbation type
may not always
im-
ply and may sometimes
hurt
robustness againstother perturbation types. In light of these results,
we recommend evaluation of adversarial defensestake place on a diverse range of perturbation types
and sizes.

SGD on Neural Networks LearnsFunctions of Increasing Complexity
Preetum Nakkiran
Harvard University
Gal Kaplun
Harvard University
Dimitris Kalimeris
Harvard University
Tristan Yang
Harvard University
Benjamin L. Edelman
Harvard University
Fred Zhang
Harvard University
Boaz Barak
Harvard University
Abstract
We perform an experimental study of the dynamics of Stochastic Gradient Descent(SGD) in learning deep neural networks for several real and synthetic classification
tasks. We show that in the initial epochs, almost all of the performance improve-ment of the classifier obtained by SGD can be explained by a linear classifier.More generally, we give evidence for the hypothesis that, as iterations progress,SGD learns functions of increasing complexity. This hypothesis can be helpful inexplaining why SGD-learned classifiers tend to generalize well even in the over-
parameterized regime. We also show that the linear classifier learned in the initial
stages is “retained” throughout the execution even if training is continued to thepoint of zero training error, and complement this with a theoretical result in asimplified model. Key to our work is a new measure of how well one classifier
explains the performance of another, based on conditional mutual information

This explanatory memorandum accompanies the proposal for a Regulation laying down
harmonised rules on artificial intelligence (Artificial Intelligence Act). Artificial Intelligence
(AI) is a fast evolving family of technologies that can bring a wide array of economic and
societal benefits across the entire spectrum of industries and social activities. By improving
prediction, optimising operations and resource allocation, and personalising service delivery,
the use of artificial intelligence can support socially and environmentally beneficial outcomes
and provide key competitive advantages to companies and the European economy. Such
action is especially needed in high-impact sectors, including climate change, environment and
health, the public sector, finance, mobility, home affairs and agriculture. However, the same
elements and techniques that power the socio-economic benefits of AI can also bring about
new risks or negative consequences for individuals or the society. In light of the speed of
technological change and possible challenges, the EU is committed to strive for a balanced
approach. It is in the Union interest to preserve the EU’s technological leadership and to
ensure that Europeans can benefit from new technologies developed and functioning
according to Union values, fundamental rights and principles.

The naval procurement deal, cut nearly a decade ago while Malaysia was still under Umno rule, has come back to haunt the party – and by extension Ismail Sabri, who as prime minister is now responsible for finding a solution to the alleged scandal that may end up implicating some of Umno’s top leadership.
At stake is voter sentiment going into a national election that Umno wants to be held this year, well ahead of the deadline of the third quarter of 2023.
In a report tabled to parliament on August 4, the powerful Public Accounts Committee (PAC) highlighted the protracted delay in delivery of the six littoral combat ships (LCS) by local contractor Boustead Naval Shipyard (BNS).
The government and BNS had in 2014 signed a record 9 billion ringgit contract for delivery of the ships, the nation’s single largest defence procurement deal. BNS was to deliver all but one of the ships to the Royal Malaysian Navy between April 2019 and August this year.
Not a single ship has been delivered to date, and the government had already paid up about two-thirds of the contract value to BNS, according to the PAC report.

In the rapidly evolving landscape of artificial intelligence, generative AI stands at the forefront, offering transformative potential across various industries. "Generative AI Business Applications" delves into this cutting-edge technology, providing a comprehensive guide for business leaders, innovators, and technologists eager to harness the power of generative models.

This book explores the foundational principles of generative AI, including neural networks, deep learning, and unsupervised learning, before transitioning to practical applications. Readers will discover how generative AI can revolutionize content creation, marketing, product design, and customer service. Through detailed case studies and real-world examples, the book illustrates how businesses are leveraging generative models to enhance creativity, optimize operations, and drive strategic decision-making.

With a focus on ethical considerations and best practices, "Generative AI Business Applications" also addresses the challenges and risks associated with deploying these technologies. By the end of the book, readers will gain a nuanced understanding of generative AI's capabilities and limitations, empowering them to implement innovative solutions that drive business growth and competitive advantage in the digital age.

Revisit the framework of Approximate Dynamic Programming. Under the 2 sources of error (estimation + function approximation), what can we say about resulting estimates? Next lectures: (more) approximate versions of these paradigms, mainly in the absence of perfect knowledge of the environment + (deep) neural networks parametrisation.

There are many ways that the Blockchain can benefit entrepreneurs beyond the fact that it’s associated with a currency that makes fraudulent chargebacks impossible. It can be used for many applications that require a reliable and tamper-resistant means of record- keeping. It can be used to give you a competitive edge in a world where the economy is becoming increasingly global and customers increasingly care about how their goods are produced and can hop from one “next big thing” to the next pretty fast. If you’re looking at the Blockchain, you probably have a few questions.

The Centre for Independent Journalism (CIJ) today launched a website to “Say No to Hate Speech” as Malaysia enters election season.

CIJ executive director Wathshlah G. Naidu encouraged Malaysians to report hate speech when they see it to the portal, which exists to educate the public on hate speech while providing general updates on how hate speech occurs and who key actors are.

“Politicians and other key actors often weaponise inflammatory tropes and rhetoric to control narratives and influence public understanding around issues like race, religion, royalty, gender and LGBTIQ, and refugees and migrants.

“They do so to advance narrow political interests that do not serve a democracy,” she said.

She added that hate speech diverts attention from solution-focused thinking while increasing the potential for harm against marginalised communities.

CIJ and its partners expect hate speech and the dissemination of disinformation to intensify on social media during GE15, she said.

She said increases have been observed to take place in past elections and when there is a shake-up of the political landscape that threatens government stability, citing the immediate days after the 'Sheraton Move' of February 2020 and during the Covid-19 pandemic lockdowns.

The project aims to monitor the severity of hate speech and to develop collective responses to hate-based narratives leading up to and during GE15, while also enabling the public to respond more effectively to encountering hate speech online.

Ryan Chua from Pusat Komas, who was also at the launch, said that the microsite aims to remind key actors that they are always under public scrutiny.

“We are watching you and as we are watching you, we are documenting it,” he said.

He said that this is because the communities often targeted by hate speech are particularly vulnerable.

The data from the project can also be used to document electoral offences, he added.

Wathshlah also indicated that those who are directly affected by hate speech will be provided certain services, including funding for their legal defence if needed.

The project is divided into two components.

The first monitoring component is a collaboration involving CIJ and three universities — Universiti Sains Malaysia, Universiti Malaysia Sabah and University of Nottingham Malaysia.

The second, a rapid response component, involves a partnership between CIJ and Pusat Komsas, Sisters in Islam, Architects of Diversity, Beyond Borders Malaysia, Asylum Access Malaysia, Sahabat Wanita, Tenaganita, North South Initiative and Justice for Sisters.

“The monitoring component will measure the severity of hate speech around race, religion, royalty, gender and LGBTIQ, and refugees and migrants, that are amplified by political parties, politicians, government agencies, media organisations and key opinion leaders,” said Wathshlah.

The project established four levels of hate speech: level one involves disagreements or non-offensive language, level two is made up of offensive or discriminatory language, level three is dehumanising or hostile language and level four is language that causes incitement or calls for violence.

The rapid response component involves an alert system build on the data from the monitoring component, which identifies when a particular expression requires some sort of response or action in a timely manner.

The site can be accessed at: https://cijmalaysia.net/election-monitoring/about/

Synthesis Lectures on Learning, Networks, and Algorithms is an ongoing series of 75-to 150-page publications on topics on the design, analysis, and management of complex networked systems using tools from control, communications, learning, optimization, and stochastic analysis. Each lecture is a self-contained presentation of one topic by a leading expert. The topics include learning, networks, and algorithms, and cover a broad spectrum of applications to networked systems including communication networks, data-center networks, social, and transportation networks. The series is designed to: • Provide the best available presentations of important aspects of complex networked systems. • Help engineers and advanced students keep up with recent developments in a rapidly evolving field of science and technology. • Facilitate the development of courses in this field.

Why, when there are various coding assets as of now, did I compose this aide? The appropriate response is straightforward: models. I have consistently learned best by concentrating on models. This is an aide for those out there like me that need to see an assignment performed before they can appropriately copy it.

Kafka Overdraft fee topic Update fee topic Overdraft topic Overdraft service 4. Overdraft events exchanged between Kafka topics and Bank microservices 2. JSON Web Tokens retrieved from Keycloak and propagated between services 1. Instrumented services traces forwarded to Jaeger 3. Metrics data pulled from instrumented services by Prometheus and graphed with Grafana quarkus_banking database