Do algorithms enhance our worst behaviors? How do algorithms influence our world views? Are social media algorithms making the world worse? Tech journalist Sinead Bovell talks with an ex-YouTube engineer, a former design ethicist from Google and an Oxford professor about the impact algorithms are having in our lives.

From the physical world to the virtual world, algorithms are seemingly everywhere. David J. Malan, Professor of Computer Science at Harvard University, has been challenged to explain the science of algorithms to 5 different people; a child, a teen, a college student, a grad student, and an expert. Director: Wendi Jonassen Director of Photography: Zach Eisen Editor: Louville Moore Host: David J. Malan Guests: Level 1: Addison Vincent Level 2: Lexi Kemmer Level 3: Patricia Guirao Level 4: Mahi Shafiullah Level 5: Chris Wiggins Creative Producer: Maya Dangerfield Line Producer: Joseph Buscemi Associate Producer: Paul Gulyas; Kameryn Hamilton Production Manager: D. Eric Martinez Production Coordinator: Fernando Davila Casting Producer: Vanessas Brown; Nicholas Sawyer Camera Operator: Brittany Berger Gaffer: Gautam Kadian Sound Mixer: Lily Van Leeuwen Production Assistant: Ryan Coppola Hair & Make-Up: Yev Wright-Mason Post Production Supervisor: Alexa Deutsch Post Production Coordinator: Ian Bryant Supervising Editor: Doug Larsen Assistant Editor: Lauren Worona

There's a special guest on the show who takes aim at the billionaires in web dev, do we know better than the algorithm for news, why is AI training data such a secret, Chris and Dave discover JetBrains, monorepo struggles, and SVG drawing tools.

Dave and Chris discuss indie web culture, the role of social media in today's society, and the challenges and strategies of freelancing. Additionally, they discuss a range of topics from content moderation, coding and refining tech skills, to emerging startups and the future of web technology.

New York : Springer, 2007

Cham, Switzerland : Springer, 2021.

This pro-bono initiative crowdsourced 4,500 drug designs, synthesized 311, and is now testing them against viral proteins

This paper describes MOLES 1.0, an integrated land-use and transport model developed with Object-Oriented Programming principles in order to combine selected characteristics from Spatial Computable General Equilibrium and microsimulation models. MOLES 1.0 models the links between urban land use, mobility patterns, urban economic activities and their environmental impacts, in particular air pollution and emissions of greenhouse gases.

An expectation maximization algorithm is implemented to resolve the indexing ambiguity which arises when merging data from many crystals in protein crystallography, especially in cases where partial reflections are recorded in serial femtosecond crystallography (SFX) at XFELs.

A new scaling program is presented with new features to support multi-sweep workflows and analysis within the DIALS software package.

In processing X-ray diffraction data, the intensities obtained from integration of the diffraction images must be corrected for experimental effects in order to place all intensities on a common scale both within and between data collections. Scaling corrects for effects such as changes in sample illumination, absorption and, to some extent, global radiation damage that cause the measured intensities of symmetry-equivalent observations to differ throughout a data set. This necessarily requires a prior evaluation of the point-group symmetry of the crystal. This paper describes and evaluates the scaling algorithms implemented within the DIALS data-processing package and demonstrates the effectiveness and key features of the implementation on example macromolecular crystallographic rotation data. In particular, the scaling algorithms enable new workflows for the scaling of multi-crystal or multi-sweep data sets, providing the analysis required to support current trends towards collecting data from ever-smaller samples. In addition, the implementation of a free-set validation method is discussed, which allows the quantification of the suitability of scaling-model and algorithm choices.

Serial crystallography records still diffraction patterns from single, randomly oriented crystals, then merges data from hundreds or thousands of them to form a complete data set. To process the data, the diffraction patterns must first be indexed, equivalent to determining the orientation of each crystal. A novel automatic indexing algorithm is presented, which in tests usually gives significantly higher indexing rates than alternative programs currently available for this task. The algorithm does not require prior knowledge of the lattice parameters but can make use of that information if provided, and also allows indexing of diffraction patterns generated by several crystals in the beam. Cases with a small number of Bragg spots per pattern appear to particularly benefit from the new approach. The algorithm has been implemented and optimized for fast execution, making it suitable for real-time feedback during serial crystallography experiments. It is implemented in an open-source C++ library and distributed under the LGPLv3 licence. An interface to it has been added to the CrystFEL software suite.

Many here in Massachusetts started social distancing about a month ago and we have no end in sight yet. If you live alone, maybe you are ready to match up with someone after you get through this hardship. Today's guest blogger, Toshi Takeuchi, has a perfect algorithm for you. I love that this was inspired by a problem that, at first glance, doesn't seem very technical or relevant. But it is!... read more >>

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Cryptographic experts at NTU Singapore and INRIA in Paris, have demonstrated a critical security flaw in a commonly used security algorithm, known as SHA-1, which would allow attackers to fake specific files and the information within them, and pass them off as authentic....

The Elbphilharmonie features over 1 million computer-generated divots to shape sound within its main auditorium.

The sieve of Eratosthenes is an ancient tool for finding primes, but it might get a boost by modern computing.

The omnipresence of algorithms raises interesting questions about day-to-day human experience.

WBD101's Second Generation 2-in-1 SBS2000 chip used by Kan Tsang New Technology Development (Kan Tsang) for True Wireless Stereo (TWS300HR) Earbuds

Investors report earning 12.5% in average monthly profits with XGEN Algorithms

Peninsula General's website continues to offer a fast, online auto insurance quote system that was released in early September 2018.

An introduction to the DBSCAN algorithm and its Implementation in Python.

Marcus Buckingham, founder of TMBC and author of "StandOut."

Cathy O'Neil, author of "Weapons of Math Destruction" on how data can lead us astray–from HR to Wall Street.

Margaret Parker, MD, MCCM, speaks with Nilesh M. Mehta

Perron-Frobenius theorem and linear algebra have many virtues to extol

-- Read more on ScientificAmerican.com

Last weekend, at the Subscribe10 conference, we released Advanced Audio Algorithm Parameters for Multitrack Productions:

We launched our advanced audio algorithm parameters for Singletrack Productions last year. Now these settings (and more) are available for Multitrack Algorithms as well, which gives you detailed control for each track of your production.

• Fore/Background Settings: keep your music/clip tracks unchanged and set a custom background gain
• Multitrack Leveler Parameters: control the stereo panorama, leveling algorithm, dynamic range and compression
• Better Hum and Noise Reduction Controls for each track
• Maximum True Peak Level setting for the final mixdown
• Full API Support

Please join our private beta program and let us know how you use these new features or if you need even more control!

Fore/Background Settings

The parameter Fore/Background controls whether a track should be in foreground, in background, ducked, or unchanged, which is especially important for music or clip tracks.
For more details, please see Automatic Ducking, Foreground and Background Tracks .

Unchanged (Foreground):

We sometimes received complaints from users, which produced very complex music or clip tracks, that Auphonic changes the levels too hard.
If you set the parameter Fore/Background to the new option Unchanged (Foreground), Level relations within this track won’t be changed at all. It will be added to the final mixdown so that foreground/solo parts of this track will be as loud as (foreground) speech from other tracks.

Background Level:

It is now possible to set the level of background segments/tracks (compared to foreground segments) in background and ducking tracks. By default, background and ducking segments are 18dB softer than foreground segments.

Leveler Parameters

Similar to our Singletrack Advanced Leveler Parameters (see this previous blog post), we also released leveling parameters for Multitrack Productions now.
The following advanced parameters for our Multitrack Adaptive Leveler can be set for each track and allow you to customize which parts of the audio should be leveled, how much they should be leveled, how much dynamic range compression should be applied and to set the stereo panorama (balance):

Leveler Preset:

Select the Speech or Music Leveler for this track.
If set to Automatic (default), a classifier will decide if this is a music or speech track.

Dynamic Range:

The parameter Dynamic Range controls how much leveling is applied: Higher values result in more dynamic output audio files (less leveling). If you want to increase the dynamic range by 3dB (or LU), just increase the Dynamic Range parameter by 3dB.
For more details, please see Multitrack Leveler Parameters.

Compressor:

Select a preset for Micro-Dynamics Compression: Auto, Soft, Medium, Hard or Off.
The Compressor adjusts short-term dynamics, whereas the Leveler adjusts mid-term level differences.
For more details, please see Multitrack Leveler Parameters.

Stereo Panorama (Balance):

Change the stereo panorama (balance for stereo input files) of the current track.
Possible values: L100, L75, L50, L25, Center, R25, R50, R75 and R100.

If you understand German and want to know more about our Advanced Leveler Parameters and audio dynamics in general, watch our talk at the Subscribe10 conference:
Video: Audio Lautheit und Dynamik.

Better Hum and Noise Reduction Controls

Noise Reduction Amount:

Maximum noise and hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides if and how much noise reduction is necessary (to avoid artifacts). Set to a custom (non-Auto) value if you prefer more noise reduction or want to bypass our classifier.

Hum Base Frequency:

Set the hum base frequency to 50Hz or 60Hz (if you know it), or use Auto to automatically detect the hum base frequency in each speech region.

Hum Reduction Amount:

Maximum hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides how much hum reduction is necessary in each speech region. Set it to a custom value (> 0), if you prefer more hum reduction or want to bypass our classifier. Use Disable Dehum to disable hum reduction and use our noise reduction algorithms only.

Behavior of noise and hum reduction parameter combinations:

Maximum True Peak Level

In the Master Algorithm Settings of your multitrack production, you can set the maximum allowed true peak level of the processed output file, which is controlled by the True Peak Limiter after our Loudness Normalization algorithms.

If set to Auto (which is the current default), a reasonable value according to the selected loudness target is used: -1dBTP for 23 LUFS (EBU R128) and higher, -2dBTP for -24 LUFS (ATSC A/85) and lower loudness targets.

Full API Support

All advanced algorithm parameters, for Singletrack and Multitrack Productions, are available in our API as well, which allows you to integrate them into your scripts, external workflows and third-party applications.

Join the Beta and Send Feedback

Please join our beta and let us know your case studies, if you need any other algorithm parameters or if you have any questions!

Here are some private beta invitation codes:

8tZPc3T9pH VAvO8VsDg9 0TwKXBW4Ni kjXJMivtZ1 J9APmAAYjT Zwm6HabuFw HNK5gF8FR5 Do1MPHUyPW CTk45VbV4t xYOzDkEnWP
9XE4dZ0FxD 0Sl3PxDRho uSoRQxmKPx TCI62OjEYu 6EQaPYs7v4 reIJVOwIr8 7hPJqZmWfw kti3m5KbNE GoM2nF0AcN xHCbDC37O5
6PabLBRm9P j2SoI8peiY olQ2vsmnfV fqfxX4mWLO OozsiA8DWo weJw0PXDky VTnOfOiL6l B6HRr6gil0 so0AvM1Ryy NpPYsInFqm
oFeQPLwG0k HmCOkyaX9R G7DR5Sc9Kv MeQLSUCkge xCSvPTrTgl jyQKG3BWWA HCzWRxSrgW xP15hYKEDl 241gK62TrO Q56DHjT3r4
9TqWVZHZLE aWFMSWcuX8 x6FR5OTL43 Xf6tRpyP4S tDGbOUngU0 5BkOF2I264 cccHS0KveO dT29cF75gG 2ySWlYp1kp iJWPhpAimF

If you have an invitation code, you can enter it here to activate the Multitrack Advanced Audio Algorithm Parameters:
Auphonic Algorithm Parameters Private Beta Activation

The web is awash with words. They’re everywhere. On websites, in emails, advertisements, tweets, pop-ups, you name it. More people are publishing more copy than at any point in history. That means a lot of information, and a lot of competition. In recent years a slew of ‘readability’ programs have appeared to help us tidy up the things we write. (Grammarly, Readable, and Yoast are just a handful that come to mind.

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information exchange. We first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to the stationary point if each local cost function is smooth and linearly to the global optimum under an additional condition that the global cost function satisfies the Polyak-{L}ojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving the linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique or finite. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations.

The main goal of this paper is to achieve a parametrization of the solution set of the truncated matricial Hausdorff moment problem in the non-degenerate and degenerate situation. We treat the even and the odd cases simultaneously. Our approach is based on Schur analysis methods. More precisely, we use two interrelated versions of Schur-type algorithms, namely an algebraic one and a function-theoretic one. The algebraic version, worked out in our former paper arXiv:1908.05115, is an algorithm which is applied to finite or infinite sequences of complex matrices. The construction and discussion of the function-theoretic version is a central theme of this paper. This leads us to a complete description via Stieltjes transform of the solution set of the moment problem under consideration. Furthermore, we discuss special solutions in detail.

We investigate the evolution in time of the position of a fixed number inthe insertion tableau when the Robinson-Schensted-Knuth algorithm is applied to asequence of random numbers. When the length of the sequence tends to infinity, a typical trajectory after scaling converges uniformly in probability to some deterministiccurve.

Quantum computing has evolved quickly in recent years and is showing significant benefits in a variety of fields. Malware analysis is one of those fields that could also take advantage of quantum computing. The combination of software used to locate the most frequent hashes and $n$-grams between benign and malicious software (KiloGram) and a quantum search algorithm could be beneficial, by loading the table of hashes and $n$-grams into a quantum computer, and thereby speeding up the process of mapping $n$-grams to their hashes. The first phase will be to use KiloGram to find the top-$k$ hashes and $n$-grams for a large malware corpus. From here, the resulting hash table is then loaded into a quantum machine. A quantum search algorithm is then used search among every permutation of the entangled key and value pairs to find the desired hash value. This prevents one from having to re-compute hashes for a set of $n$-grams, which can take on average $O(MN)$ time, whereas the quantum algorithm could take $O(sqrt{N})$ in the number of table lookups to find the desired hash values.

This paper considers the multi-group multicast beamforming optimization problem, for which the optimal solution has been unknown due to the non-convex and NP-hard nature of the problem. By utilizing the successive convex approximation numerical method and Lagrangian duality, we obtain the optimal multicast beamforming solution structure for both the quality-of-service (QoS) problem and the max-min fair (MMF) problem. The optimal structure brings valuable insights into multicast beamforming: We show that the notion of uplink-downlink duality can be generalized to the multicast beamforming problem. The optimal multicast beamformer is a weighted MMSE filter based on a group-channel direction: a generalized version of the optimal downlink multi-user unicast beamformer. We also show that there is an inherent low-dimensional structure in the optimal multicast beamforming solution independent of the number of transmit antennas, leading to efficient numerical algorithm design, especially for systems with large antenna arrays. We propose efficient algorithms to compute the multicast beamformer based on the optimal beamforming structure. Through asymptotic analysis, we characterize the asymptotic behavior of the multicast beamformers as the number of antennas grows, and in turn, provide simple closed-form approximate multicast beamformers for both the QoS and MMF problems. This approximation offers practical multicast beamforming solutions with a near-optimal performance at very low computational complexity for large-scale antenna systems.

The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical mixing time. In this article, we deal with analog quantum algorithms for mixing. First, we provide an analog quantum algorithm that given a Markov chain, allows us to sample from its stationary distribution in a time that scales as the sum of the square root of the classical mixing time and the square root of the classical hitting time. Our algorithm makes use of the framework of interpolated quantum walks and relies on Hamiltonian evolution in conjunction with von Neumann measurements.

There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense. In this scenario, the quantum mixing time is defined as the time required to sample from a distribution that is close to this limiting distribution. Recently we provided an upper bound on the quantum mixing time for Erd"os-Renyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we also extend and expand upon our findings therein. Namely, we provide an intuitive understanding of the state-of-the-art random matrix theory tools used to derive our results. In particular, for our analysis we require information about macroscopic, mesoscopic and microscopic statistics of eigenvalues of random matrices which we highlight here. Furthermore, we provide numerical simulations that corroborate our analytical findings and extend this notion of mixing from simple graphs to any ergodic, reversible, Markov chain.

The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) scheme is used extensively in astrophysics for data collection and analysis on the sphere. The scheme was originally designed for studying the Cosmic Microwave Background (CMB) radiation, which represents the first light to travel during the early stages of the universe's development and gives the strongest evidence for the Big Bang theory to date. Refined analysis of the CMB angular power spectrum can lead to revolutionary developments in understanding the nature of dark matter and dark energy. In this paper, we present a new method for performing spherical harmonic analysis for HEALPix data, which is a central component to computing and analyzing the angular power spectrum of the massive CMB data sets. The method uses a novel combination of a non-uniform fast Fourier transform, the double Fourier sphere method, and Slevinsky's fast spherical harmonic transform (Slevinsky, 2019). For a HEALPix grid with $N$ pixels (points), the computational complexity of the method is $mathcal{O}(Nlog^2 N)$, with an initial set-up cost of $mathcal{O}(N^{3/2}log N)$. This compares favorably with $mathcal{O}(N^{3/2})$ runtime complexity of the current methods available in the HEALPix software when multiple maps need to be analyzed at the same time. Using numerical experiments, we demonstrate that the new method also appears to provide better accuracy over the entire angular power spectrum of synthetic data when compared to the current methods, with a convergence rate at least two times higher.

We investigate sparse matrix bipartitioning -- a problem where we minimize the communication volume in parallel sparse matrix-vector multiplication. We prove, by reduction from graph bisection, that this problem is $mathcal{NP}$-complete in the case where each side of the bipartitioning must contain a linear fraction of the nonzeros.

We present an improved exact branch-and-bound algorithm which finds the minimum communication volume for a given matrix and maximum allowed imbalance. The algorithm is based on a maximum-flow bound and a packing bound, which extend previous matching and packing bounds.

We implemented the algorithm in a new program called MP (Matrix Partitioner), which solved 839 matrices from the SuiteSparse collection to optimality, each within 24 hours of CPU-time. Furthermore, MP solved the difficult problem of the matrix cage6 in about 3 days. The new program is on average more than ten times faster than the previous program MondriaanOpt.

Benchmark results using the set of 839 optimally solved matrices show that combining the medium-grain/iterative refinement methods of the Mondriaan package with the hypergraph bipartitioner of the PaToH package produces sparse matrix bipartitionings on average within 10% of the optimal solution.

This paper is the first to consider online algorithms to schedule a proportionate flexible flow shop of batching machines (PFFB). The scheduling model is motivated by manufacturing processes of individualized medicaments, which are used in modern medicine to treat some serious illnesses. We provide two different online algorithms, proving also lower bounds for the offline problem to compute their competitive ratios. The first algorithm is an easy-to-implement, general local scheduling heuristic. It is 2-competitive for PFFBs with an arbitrary number of stages and for several natural scheduling objectives. We also show that for total/average flow time, no deterministic algorithm with better competitive ratio exists. For the special case with two stages and the makespan or total completion time objective, we describe an improved algorithm that achieves the best possible competitive ratio $varphi=frac{1+sqrt{5}}{2}$, the golden ratio. All our results also hold for proportionate (non-flexible) flow shops of batching machines (PFB) for which this is also the first paper to study online algorithms.

In this paper, we present three distributed algorithms to solve a class of generalized Nash equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the second and the third (AD-GEED and AD-GENO) represent asynchronous solutions that are robust to communication delays. AD-GENO can be seen as a refinement of AD-GEED, since it only requires node auxiliary variables, enhancing the scalability of the algorithm. Our main contribution is to prove converge to a variational GNE of the game via an operator-theoretic approach. Finally, we apply the algorithms to network Cournot games and show how different activation sequences and delays affect convergence. We also compare the proposed algorithms to the only other in the literature (ADAGNES), and observe that AD-GENO outperforms the alternative.

For any finite Galois field extension $mathsf{K}/mathsf{F}$, with Galois group $G = mathrm{Gal}(mathsf{K}/mathsf{F})$, there exists an element $alpha in mathsf{K}$ whose orbit $Gcdotalpha$ forms an $mathsf{F}$-basis of $mathsf{K}$. Such an $alpha$ is called a normal element and $Gcdotalpha$ is a normal basis. We introduce a probabilistic algorithm for testing whether a given $alpha in mathsf{K}$ is normal, when $G$ is either a finite abelian or a metacyclic group. The algorithm is based on the fact that deciding whether $alpha$ is normal can be reduced to deciding whether $sum_{g in G} g(alpha)g in mathsf{K}[G]$ is invertible; it requires a slightly subquadratic number of operations. Once we know that $alpha$ is normal, we show how to perform conversions between the working basis of $mathsf{K}/mathsf{F}$ and the normal basis with the same asymptotic cost.

One of the main open problems in the qualitative theory of real planar differential systems is the study of limit cycles. In this article, we present an algorithmic approach for detecting how many limit cycles can bifurcate from the periodic orbits of a given polynomial differential center when it is perturbed inside a class of polynomial differential systems via the averaging method. We propose four symbolic algorithms to implement the averaging method. The first algorithm is based on the change of polar coordinates that allows one to transform a considered differential system to the normal form of averaging. The second algorithm is used to derive the solutions of certain differential systems associated to the unperturbed term of the normal of averaging. The third algorithm exploits the partial Bell polynomials and allows one to compute the integral formula of the averaged functions at any order. The last algorithm is based on the aforementioned algorithms and determines the exact expressions of the averaged functions for the considered differential systems. The implementation of our algorithms is discussed and evaluated using several examples. The experimental results have extended the existing relevant results for certain classes of differential systems.

Consider the following prediction problem. Assume that there is a block box that produces bits according to some unknown computable distribution on the binary tree. We know first $n$ bits $x_1 x_2 ldots x_n$. We want to know the probability of the event that that the next bit is equal to $1$. Solomonoff suggested to use universal semimeasure $m$ for solving this task. He proved that for every computable distribution $P$ and for every $b in {0,1}$ the following holds: $$sum_{n=1}^{infty}sum_{x: l(x)=n} P(x) (P(b | x) - m(b | x))^2 < infty .$$ However, Solomonoff's method has a negative aspect: Hutter and Muchnik proved that there are an universal semimeasure $m$, computable distribution $P$ and a random (in Martin-L{"o}f sense) sequence $x_1 x_2ldots$ such that $lim_{n o infty} P(x_{n+1} | x_1ldots x_n) - m(x_{n+1} | x_1ldots x_n) rightarrow 0$. We suggest a new way for prediction. For every finite string $x$ we predict the new bit according to the best (in some sence) distribution for $x$. We prove the similar result as Solomonoff theorem for our way of prediction. Also we show that our method of prediction has no that negative aspect as Solomonoff's method.

Most optimization-based community detection approaches formulate the problem in a single or bi-objective framework. In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. In the first variant, named NSGA-III-KRM, we considered Kernel k means, Ratio cut, and Modularity, as the three objectives, whereas the second variant, named NSGA-III-CCM, considers Community score, Community fitness and Modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art approaches along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the addition of the third objective does not worsen the results of the other two objectives. We also propose a simple method to rank the Pareto solutions so obtained by proposing a new measure, namely the ratio of the hyper-volume and inverted generational distance (IGD). The higher the ratio, the better is the Pareto set. This strategy is particularly useful in the absence of empirical attainment function in the multi-objective framework, where the number of objectives is more than two.

In this technical report we give an elementary introduction to Quantum Computing for non-physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor's Algorithm, Grocer Search, and Quantum Counting Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we give an introduction to Solomonoff Induction, a theoretically optimal method for prediction. We then attempt to use Quantum computing to find better algorithms for the approximation of Solomonoff Induction. This is done by using techniques from other Quantum computing algorithms to achieve a speedup in computing the speed prior, which is an approximation of Solomonoff's prior, a key part of Solomonoff Induction. The major limiting factors are that the probabilities being computed are often so small that without a sufficient (often large) amount of trials, the error may be larger than the result. If a substantial speedup in the computation of an approximation of Solomonoff Induction can be achieved through quantum computing, then this can be applied to the field of intelligent agents as a key part of an approximation of the agent AIXI.

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs well in various kinds of single-task optimization and yields promising results in comparison with the canonical genetic algorithm. However, LTGA is an unsuitable method for dealing with multi-task optimization problems. On the other hand, Multifactorial Optimization (MFO) can simultaneously solve independent optimization problems, which are encoded in a unified representation to take advantage of the process of knowledge transfer. In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able to tackle multiple optimization tasks at the same time, each task learns the dependency between problem variables from the shared representation. This knowledge serves to determine the high-quality partial solutions for supporting other tasks in exploring the search space. Moreover, MF-LTGA speeds up convergence because of knowledge transfer of relevant problems. We demonstrate the effectiveness of the proposed algorithm on two benchmark problems: Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or in computation time.