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Transforming Healthcare: The Impact of Machine Learning on Patient Care

Consider a world in which receiving healthcare is a proactive, individualized experience tailored to each individuals exact needs rather than a reactive response to illness. Let me introduce you to machine learning, a technological marvel that is transforming healthcare. This article will look at the broad benefits of machine learning in healthcare, such as improved diagnostics, personalized treatment regimens, predictive analytics, and more.

Lets start with the basics. What is machine learning, and how is it being used in the healthcare industry? The machine learning discipline of artificial intelligence enables computers to learn and make decisions without the need for explicit programming. This refers to the use of algorithms to evaluate enormous amounts of data and turn it into insights that can be implemented. This results in better communication amongst healthcare workers and more effective study of medical material.

Better Diagnosis and Timely Identification

The application of machine learning to early detection and diagnosis in healthcare is among its most important contributions. These days, algorithms can analyze medical pictures like X-rays and MRIs with a precision that matches or frequently exceeds that of human analysts.

Dr Emily Harris, a leading radiologist, attests to the transformative impact: "Machine learning algorithms have become invaluable in our diagnostic process. They can identify subtle patterns and anomalies in medical images that might escape the human eye. This not only accelerates the diagnostic process but also enhances accuracy, leading to more effective treatment plans."

Tailored Care Programs

Machine learning is about more than just diagnosing; its about customizing care for each patient. Healthcare providers can now develop tailored drug regimens by utilizing genetic and patient data. For instance, this has created new opportunities for targeted medicines that optimize efficacy while minimizing negative effects in the field of cancer treatment.

Dr Sarah Thompson, a customized medicine-focused oncologist, clarifies: "Machine learning allows us to sift through an immense amount of genetic data to identify specific mutations driving a patients cancer. This knowledge enables us to prescribe treatments that precisely target these mutations, ushering in a new era of precision medicine."

Preventive Measures and Predictive Analytics

Envision a healthcare system that anticipates and averts illnesses in addition to providing treatment for them. This vision is becoming a reality thanks to machine learning. These algorithms forecast disease outbreaks, identify high-risk individuals, and suggest preventive measures based on past health data analysis.

The importance is emphasized by data scientist John Davis, who works on predictive analytics: "Our models can predict the likelihood of a patient developing certain conditions based on their health history." This enables people to make knowledgeable lifestyle decisions that can improve their health and permits early intervention."

Management of Electronic Health Records (EHR)

Handling Electronic Health Records (EHR) effectively is essential to delivering smooth and well-coordinated patient care. EHR systems are becoming more efficient because of machine learning, which is also improving data accessibility and guaranteeing platform interoperability. This enhances the general effectiveness of healthcare delivery and moves the needle toward a patient-centric methodology.

But even as we welcome these technical developments, we also need to address privacy and security issues. Finding the ideal balance between innovation and patient data security is a constant struggle that needs considerable thought.

Difficulties and Ethical Issues

Even though machine learning has many advantages in healthcare, its important to recognize the difficulties and moral dilemmas that come with this technological revolution. We need to pay attention to issues like algorithmic bias, patient privacy, and decision-making procedures' transparency.

Health technology ethicist Dr. James Miller issues the following caution: "We must emphasize ethical considerations as we integrate machine learning into patient care. Establishing transparency, equity, and adherence to patient privacy is crucial in fostering confidence in new technologies."

Future Innovations and Trends

This is not where the journey ends. Prospects for machine learning appear to have even more innovation potential. Future developments like quantum computing, federated learning, and reinforcement learning have the potential to completely alter the landscape of healthcare.

Focusing on the future, scholar Dr. Sophia Chen says the following about healthcare technology: "A new era of healthcare will be ushered in by the integration of advanced machine learning techniques." A more intelligent, patient-centred, networked system that adjusts to each persons requirements and preferences is what were heading toward."

To sum up, machine learning is more than just a catchphrase; its a revolutionary force that is changing healthcare as we know it. Improved diagnostics, tailored treatment regimens, predictive analytics, and more are just a few of the noticeable and extensive effects. To maintain a bright, egalitarian, and patient-centred future for healthcare, we must welcome innovation while respecting ethical principles as we traverse this technological frontier.

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Transforming Healthcare: The Impact of Machine Learning on Patient Care - Medium

The advent of technology has revolutionized many aspects of our lives, and healthcare is no exception. Among the most promising advancements in this field is the integration of Artificial Intelligence (AI) and Machine Learning (ML), particularly in the diagnosis and management of heart disease. This shift towards AI-based healthcare solutions promises improved accuracy, efficiency, and precision in diagnosing heart conditions, heralding a significant leap forward in both early detection and treatment management of heart disease.

Artificial Intelligence has shown great promise in the early detection of congenital heart diseases in neonates, significantly impacting pediatric healthcare. According to a review of data published between 2015 and 2023, AI has improved the accuracy and efficiency of diagnosing congenital heart diseases. The technology demonstrated high sensitivity and specificity, indicating its potential for broad application in neonatal care. However, like any technological advancement, AI also presents certain challenges that need to be addressed for its successful implementation.

Further reinforcing AIs potential, a study explored the feasibility of automatic diagnosis of congenital heart disease (CHD) and pulmonary arterial hypertension (PAH) associated with CHD using AI technology. The study utilized AI models trained with chest radiographs to identify CHD and PAH CHD. The results were impressive, with the AI model achieving an average area under the receiver operating characteristic curve (AUC) of 0.948 for CHD diagnoses and an AUC of 0.778 for identifying PAH CHD. In addition, the study found that the diagnostic accuracy of radiologists significantly improved when they were given AI-based classifications.

Natural Language Processing (NLP), a subfield of AI, has shown potential in improving the detection and diagnosis of Heart Failure with preserved Ejection Fraction (HFpEF). A retrospective cohort study used an NLP pipeline applied to the Electronic Health Record (EHR) to identify patients with a clinical diagnosis of HF between 2010 and 2022. The study found that patients with undiagnosed HFpEF are an at-risk group with high mortality. This underlines the importance of early detection and diagnosis, which NLP can facilitate by identifying likely HFpEF patients from EHR data. These patients could benefit significantly from an expert clinical review and the use of diagnostic algorithms.

Given the promising results of AI in detecting and diagnosing heart diseases, its clear that this technology will play a significant role in the future of healthcare. AIs ability to enhance the accuracy and efficiency of diagnoses can lead to more precise treatment recommendations, potentially saving more lives. However, its crucial to address the challenges that come with AI, such as ethical considerations, data security, and the need for regulation. With strategic planning and careful implementation, AI can undoubtedly revolutionize the future of heart disease diagnosis, contributing to a healthier world.

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The Role of Artificial Intelligence and Machine Learning in Heart Disease Diagnosis - Medriva

Data is the driver of decision-making and innovation in todays world, and the race to harness its potential is a journey of continual learning and collaboration. This is the cornerstone of machine learning, a field that thrives on the ability to interpret complex data and convert it into actionable insights. The aspiration to excel in this domain often leads enthusiasts to a crossroad: a quest to find a way to not only challenge their skills but also nurture them. This is where bitgrit steps in, offering a confluence of competition, learning, earning, and real-world problem-solving.

Bitgrit is a competitive space, community, and marketplace for data scientists and AI enthusiasts to demonstrate, hone, and monetize their skills through various competitions. These competitions are a gateway to a world where real-world problems posed by companies become challenges awaiting solutions from a global community of data scientists.

With each competition, bitgrit aims to accelerate the journey of discovery and solution creation, advancing machine learning through challenges.

Bitgrits ecosystem is a meticulously crafted space fostering innovation, community engagement, and real-world problem-solving. It seamlessly intertwines blockchain technology and AI, creating an environment where data scientists can interact, learn, and contribute, while businesses access a reservoir of AI expertise. Utilizing blockchains distributed ledger technology (DLT), bitgrit records user contributions to competitions and projects, ensuring fair revenue share upon completion, which underpins the platforms commitment to transparency and fair compensation.

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With the vision of democratizing AI and a mission to evolve into a global platform congregating data scientists for societal and business betterment, bitgrit is a thriving community where daily engagements lead to earning, learning, sharing, and growth.

At the core of this ecosystem is the BGR token, streamlining various interactions within the community. Whether its earning tokens through competition engagement, transacting in the AI Marketplace, or accessing the job board, the BGR token is a catalyst in fostering a vibrant and engaging community.

The bitgrit ecosystem, a harmonious blend of competitions, a job board, an AI Marketplace, and community-driven forums, offers data scientists a platform to either kickstart or propel their careers forward. It stands as a bridge between real-world business challenges and innovative AI solutions, driving the field of AI and machine learning forward with a community of passionate data scientists.

With future integrations like discussion forums, user wallet integration, and a refined API for blockchain-based data transactions, bitgrit is poised not just as a hub for AI and machine learning, but a cornerstone for collaborative innovation in the data science realm.

The bitgrit community is more than just a gathering of data enthusiasts. With a strong community of more than 30,000 engineers worldwide, its a rich reservoir of talent and innovative AI and ML solutions that businesses can tap into. The platforms dual nature, as both a competition arena and a recruiting hub, has proven to be a potent tool for companies seeking to solve complex problems or acquire top-tier talent.

Several notable enterprises have successfully utilized the bitgrit platform to advance their machine learning initiatives. Companies like SoftBank, Atrae, CTRL-F, and even governmental bodies like NIH/NASA have engaged the bitgrit community to solve complex challenges. The collaborative competitions on bitgrit not only provided needed solutions but also offered a window into the pool of talent available for recruitment.

For instance, one of the competitions hosted on bitgrit was the NASA Tournament Lab competition, a collaborative endeavor co-orchestrated with NCATS (The National Center for Advancing Translational Sciences) and NLM (National Library of Medicine). This competition showcased bitgrits potential as a conduit linking scientific institutions with a global community of data scientists. Through this engagement, the combined intellect of the community was harnessed to develop solutions that accelerated scientific research in medicine, particularly leveraging the capabilities of natural language processing.

Furthermore, the success stories extend to the individual level as well. Competition winners not only clinch financial rewards but also catch the eye of potential employers. The platform serves as a launchpad, propelling winners into the radar of companies keen on harnessing fresh, innovative minds to further their projects. Some of these companies have gone the extra mile to interview winners, seeking deeper insights into the problems tackled during the competitions, and exploring possibilities of future collaborations.

The synergy between bitgrit and the participating companies creates a win-win scenario. Companies get to solve real-world problems and scout for talented data scientists, while the community members get to work on exciting projects, expand their professional network, earn and advance their careers.

The journey of machine learning is a continual quest for knowledge, innovation, and practical solutions to real-world challenges. bitgrit serves as a catalyst in this journey, offering a way for data and ML enthusiasts to grow through a unique blend of competition, collaboration, and community. Through bitgrit competitions, data scientists can challenge their skills, engage in practical problem-solving, earn, and connect with a global network of like-minded individuals and prospective employers.

Businesses, on the other hand, find a treasure trove of talent and innovative solutions that can significantly drive their projects forward. The bitgrit platform not only accelerates the advancement of machine learning projects but also nurtures a community thats geared towards making meaningful contributions to the broader data science ecosystem.

As bitgrit continues to evolve, incorporating blockchain technology and expanding its community, its carving a niche as a reputable platform where machine learning and data science thrive.

Join the bitgrit community and participate in the live bitgrit AI-Generated Text Identification Challenge. Discover the different opportunities awaiting you in the realm of machine learning and data science.

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Disclaimer: This content is informational and should not be considered financial advice. The views expressed in this article may include the author's personal opinions and do not reflect The Crypto Basics opinion. Readers are encouraged to do thorough research before making any investment decisions. The Crypto Basic is not responsible for any financial losses.

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Magnetic multi-component moment tensor potential (mMTP)

The concept of magnetic multi-component Moment Tensor Potential (mMTP) presented in the current research is based on the previously developed non-magnetic MTP for multi-component systems41,42 and magnetic MTP for single-component systems35.

The mMTP potential is local, i.e., the energy of the atomistic system is a sum of energies of individual atoms:

$$begin{aligned} E = sum _{i=1}^{N_a}E_i, end{aligned}$$

(1)

where i stands for the individual atoms in an (N_a)-atom system. We note that any configuration includes lattice vectors ({{varvec{L}}} = {{{varvec{l}}}_1,{{varvec{l}}}_2,{{varvec{l}}}_3}), atomic positions ({{varvec{R}}} = {{{varvec{r}}}_1, ldots , {{varvec{r}}}_{N_a}}), types (Z = {z_1,ldots ,z_{N_{a}}}) (we also denote (N_{rm types}) by the total number of atomic types in the system), and magnetic moments (M = {m_1,ldots ,m_{N_a}}). The energy of the atom (E_i), in turn, has the form:

$$begin{aligned} E_i = sum _{alpha =1}^{alpha _{rm max}} xi _{alpha }B_{alpha }({mathfrak n}_i), end{aligned}$$

(2)

where ({{varvec{xi }}} = {xi _{alpha } }) are the linear parameters to be optimized and (B_alpha) are the so-called basis functions, which are contractions of the descriptors25 of atomistic environment ({mathfrak n}_i), yielding a scalar. The (alpha _text {max}) parameter can be changed to provide potentials with different amount of parameters35.

The descriptors are composed of the radial part, i.e., the scalar function depending on the interatomic distances and atomic magnetic moments, and the angular part, which is a tensor of rank (nu):

$$begin{aligned} M_{mu ,nu }({mathfrak n}_i)=sum _{j} f_{mu }(| {{varvec{r}}}_{ij}|,z_i,z_j,m_i,m_j)underbrace{{{varvec{r}}}_{ij}otimes ...otimes {{varvec{r}}}_{ij}}_nu text { times }, end{aligned}$$

(3)

where ({mathfrak n}_i) stands for the atomic environment, including all the atoms within the (R_text {cut}) distance (or less) from the central atom i, (mu) is the number of the radial function, (nu) is the rank of the angular part tensor, (|{{varvec{r}}}_{ij}|) is the distance between the atoms i and j, (z_i) and (z_j) are the atomic types, (m_i) and (m_j) are the magnetic moments of the atoms.

The radial functions are expanded in a basis of Chebyshev polynomials:

$$begin{aligned} f_{mu }(|r_{ij}|,z_i,z_j,m_i,m_j) = sum _{zeta =1}^{N_{phi }} sum _{beta =1}^{N_{psi }}sum _{gamma =1}^{N_{psi }}c_{mu ,z_i,z_j}^{zeta ,beta ,gamma } phi _{zeta }(|{varvec{r}}_{ij}|) psi _{beta }(m_i)psi _{gamma }(m_j) (R_{rm cut} - |{varvec{r}}_{ij}|)^2. end{aligned}$$

(4)

Here ({{varvec{c}}} = {c_{mu ,z_i,z_j}^{zeta ,beta ,gamma }}) are the radial parameters to be optimized, each of the functions (phi _{zeta }(|{varvec{r}}_{ij}|)), (psi _{beta }(m_i)), (psi _{gamma }(m_i)) is a Chebyshev polynomial of order (zeta), (beta) and (gamma) correspondingly, taking values from (-1) to 1. The function (phi _{zeta }(|{varvec{r}}_{ij}|)) yields the dependency on the distance between the atoms i and j, while the functions (psi _{beta }(m_i)) and (psi _{gamma }(m_j)) yield the dependency on the magnetic moments of the atoms i and j, correspondingly. The arguments of the functions (phi _{zeta }(|{varvec{r}}_{ij}|)) are on the interval ((R_{rm min},R_{rm cut})), where (R_{rm min}) and (R_{rm cut}) are the minimum and maximum distance, correspondingly, between the interacting atoms. The functions (psi _{beta }(m_i)) and (psi _{gamma }(m_j)) are of the same structure, which we explain for the case of the former one. The argument of the function (psi _{beta }(m_i)) is the magnetic moment of the atom i, taking the values on the ((-M_{rm max}^{z_i},M_{rm max}^{z_i})) interval. The value (M_{rm max}^{z_i}) itself depends on the type of atom (z_i), and is determined as the maximal absolute value of the magnetic moment for atom type (z_i) in the training set. Similar to the conventional MTP, the term ((R_{rm cut} - |{varvec{r}}_{ij}|)^2) provides smooth fading to 0 when approaching the (R_{rm cut}) distance, in accordance with the locality principle (1).

We note that magnetic degrees of freedom (m_i) from (4) are collinear, i.e., they can take negative or positive values as projection onto the Z axis (though the choice of the axis is arbitrary). This way, in comparison to non-magnetic atomistic systems with N atoms, in which the amount of degrees of freedom equals 4N (namely 3N for coordinates and N for types), for the description of magnetic systems additional N degrees of freedom are introduced, standing for the magnetic moment (m_i) of each atom. The amount of parameters entering the radial functions (Eq. 4) also increases in mMTP compared to the conventional MTP41,42. Namely, in MTP this number equals (N_{mu } cdot N_{phi } cdot N_{rm types}^2), while in mMTP it is (N_{mu } cdot N_{phi } cdot N_{rm types}^2 cdot N_{psi }^2). Thus, if we take (N_{psi } = 2) (which is used in the current research), the amount of the parameters entering the radial functions would be four times more in mMTP then in MTP.

We denote all the mMTP parameters by ({varvec{theta }}= {{varvec{xi }}, {varvec{c}} }) and the total energy (1) of the atomic system by (E=E({{varvec{theta }}})=E({{varvec{theta }}};M)=E({{varvec{theta }}};{{varvec{L}}},{{varvec{R}}},Z,M)).

The tensor (Eq. (4)) includes collinear magnetic moments in its functional form. However, it is not invariant with respect to the inversion of magnetic moments, i.e., (E({{varvec{theta }}};M) ne E({{varvec{theta }}};-M)), while both original and spin-inverted configurations should yield the same energy due to the arbitrary orientation of the projection axis, which we further call the magnetic symmetry.

We use data augmentation followed by explicit symmetrization with respect to magnetic moments to train a symmetric mMTP as we discuss below. Assume we have K configurations in the training set with DFT energies (E_k^{rm DFT}), forces ({varvec{f}}^{rm DFT}_{i,k}), and stresses (sigma ^{rm DFT}_{ab,k}) ((a,b=1,2,3)) calculated. We find the optimal parameters (bar{{{varvec{theta }}}}) (fit mMTP) by minimizing the objective function:

$$begin{aligned} &sum _{k=1}^{K} Biggl [ w_{rm e} Biggl | frac{E_k ({varvec{theta }}; M) + E_{k}({varvec{theta }}; -M)}{2} - E_{k}^{rm DFT}Biggr |^2 \&quad + w_{rm f} sum _{i=1}^{N_a} Biggl | frac{{varvec{f}}_{i,k}({varvec{theta }};M) + {varvec{f}}_{i,k}({varvec{theta }};-M)}{2} - {varvec{f}}^{rm DFT}_{i,k}Biggr |^2 \&quad +w_{rm s} sum _{a,b=1}^{3} Biggl | frac{sigma _{ab,k}({varvec{theta }};M)+sigma _{ab,k}({varvec{theta }};-M)}{2} -sigma ^{rm DFT}_{ab,k}Biggr |^2 Biggr ], end{aligned}$$

(5)

where (w_{rm e}), (w_{rm f}), and (w_{rm s}) are non-negative weights. By minimizing (5) we find such optimal parameters (bar{{{varvec{theta }}}}) that yield (E_k (bar{{varvec{theta }}}; M) approx E_k (bar{{varvec{theta }}}; -M)), (k = 1, ldots , K) (the same fact takes place for the mMTP forces and stresses), i.e., we symmetrize the training set to make mMTP learn the required symmetry from the data itselfthis is called data augmentation.

Next, we modify mMTP to make the energy used for the simulations (e.g., relaxation of configurations) to satisfy the exact symmetry:

$$begin{aligned} E^{rm symm}(bar{{{varvec{theta }}}};M) = dfrac{E(bar{{varvec{theta }}};M)+E(bar{{varvec{theta }}};-M)}{2}. end{aligned}$$

(6)

That is, we substitute the mMTP energy (1) into (6) and get a functional form which satisfies the exact identity (E^{rm symm}(bar{{{varvec{theta }}}};M) = E^{rm symm}(bar{{{varvec{theta }}}};-M)) for any configuration. We also note that (E (bar{{varvec{theta }}}) approx E^{rm symm}(bar{{{varvec{theta }}}})).

We use the cDFT approach with hard constraints(i.e., Lagrange multiplier) as proposed by Gonze et al. in Ref.19. One way to formulate it is to first note that in a single-point DFT calculation we minimize the Kohn-Sham total energy functional (E[rho ; {{varvec{R}}}]) with respect to the electronic density (rho =rho (r)) (here (rho) combines the spin-up and spin-down electron densities), keeping the nuclei position ({{varvec{R}}}) fixed. In other words, we solve the following minimization problem:

$$begin{aligned} E_{rm DFT}({{varvec{R}}}) = min _rho E[rho ; {{varvec{R}}}], end{aligned}$$

and from the optimal (rho ^* = mathrm{arg,min} E[rho ; {{varvec{R}}}]) we can, e.g., find magnetization (m(r) = rho ^*_+ - rho ^*_-), where the subscripts denote the spin-up ((+)) and spin-down () densities. The magnetic moment of the ith atom can be found by integrating m(r) over some (depending on the partitioning scheme) region around the atom:

$$begin{aligned} m_i = int _{Omega _i} m(r) textrm{d}r. end{aligned}$$

(7)

Since the minimizer (rho ^*) depends on ({{varvec{R}}}), (m_i) are also the functions of ({{varvec{R}}}).

According to the cDFT approach19, we now formulate the problem of minimizing (E[rho ; {{varvec{R}}}]) in which not only ({{varvec{R}}}),but also (rho) is allowed to change only subject to constraints (7):

$$begin{aligned} begin{array}{rcl} E_{rm cDFT}(rho, {{varvec{R}}}, M) =&{} min _rho &{} E[rho ; {{varvec{R}}}] \ &{} text {subject to} &{} m_i = int _{Omega _i} big (rho _{+}(r)-rho _-(r)big ) textrm{d}r. end{array} end{aligned}$$

The algorithmic details of how this minimization problem is solved, and how the energy derivatives (forces, stresses, torques) are computed, are described in detail in Ref.19.

We used the ABINIT code43,44 for DFT (and cDFT recently developed and described in Ref.19) calculations with (6times 6times 6) k-point mesh and cutoff energy of 25 Hartree (about 680 eV). We utilized the PAW PBE method with the generalized gradient approximation. We applied constraints on magnetic moments of all atoms during cDFT calculations.

We fitted an ensemble of five mMTPs with 415 parameters in order to quantify the uncertainty of mMTPs predictions. For each mMTP we took (R_{rm min} = 2.1 ~) , (R_{rm cut} = 4.5 ~), (M_{rm max}^{rm Al} = 0.1 ~mu _B), and (M_{rm max}^{rm Fe} = 3.0 ~mu _B). The weights in the objective function (5) were (w_{rm e} = 1), (w_{rm f} = 0.01) (^2), and (w_{rm s} = 0.001).

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Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of FeAl | Scientific ... - Nature.com

A research team have built a virtual creature mimicking the many brain-containing limbs of an octopus.

Hard, clunky robots often struggle to get around the world as well as animals do. Soft, bendable materials, which better simulate natural muscles, are hailed by roboticists as the key to building more adaptable machines, but because they can move in so many different ways, they are extremely challenging to control.

Evolution already figured out how to control soft materials, so Mattia Gazzola, a mechanical engineering professor at the University of Illinois Urbana-Champaign, turned to nature for inspiration. All sorts of creatures play tricks to minimize their computing requirements, he said. Theres this mechanical intelligence in the body itself.

No animal embodies the meticulous coordination of soft limbs like the octopus. Theyre known for their intelligence and creativity, driven not by one brain, but many.

Octopuses have a highly distributed nervous system, with one brain housed in their mantle (the animals blob-like body) performing high-level functions like learning and decision-making, and neural tissue in each limb running more basic motor commands on their own.

Inspired by this hierarchical brain structure a central controller managing actions powered by the limbs Gazzolas team, led by Ph.D. student Chia-Hsien Shih, built an octopus of their own. In a study published in Advanced Intelligent Systems, they present the CyberOctopus, a simulated multi-limbed soft robot that harnesses a hierarchical machine learning strategy to forage for virtual treats.

Each arm of the CyberOctopus was modeled as a bendable rod enveloped by elastic virtual muscles. By activating different combinations of these muscles, the limb can contract, bend, extend, or twist. Traveling waves of muscle contractions can undulate the arms to pull the creature across the floor of a virtual environment, or grab a food target and bring it to its mouth.

One common approach in machine learning and robotics, Gazzola said, is to throw a huge neural network at the system and hope it learns what to do. This can work in simple environments where there are a limited number of possible actions and outcomes for the robot to experience. But here, he said, there are too many variables to deal with. We tried, and it just doesnt work.

Instead, Shih and Gazzola designed a three-tiered control system to guide the CyberOctopus. The lowest level deals with obstacles with basically no computation at all, instructing limb muscles to reflexively relax when they bump into something.

Above that, each individual limb employs two simple algorithms that allow it to do two basic behaviors on its own: reach for a food target and crawl. At the top, a more complex algorithm tries to create an optimal sequence of those two behaviors reach and crawl to gather as much food as possible while using as little energy as possible.

After confirming that they could activate the muscles required to make the model crawl around a virtual environment, they challenged it with progressively harder food-gathering tasks.

The researchers tracked how much energy the octopus spent relative to how much it regained by eating, and they found that their hierarchical control technique successfully guided the CyberOctopus through its foraging challenges all without relying on massive neural networks.

Theres a tendency nowadays to use neural networks for everything, Gazzola said. They are very powerful tools. But if you understand the physical problem, then you can leverage that understanding to your advantage.

While the CyberOctopus succeeded at the tasks it faced here, the model still doesnt hold a candle to the creative problem-solving abilities of a real octopus. Robots capable of figuring out, on the fly, how to escape from a virtual aquarium in such a big parameter space, in a complex environment, are not there yet, Gazzola said.

But materials science, robotics, and machine learning techniques are getting more powerful by the day, so a CyberOctopus imbued with true octopus-like intelligence may be possible down the line.

Moving forward, Gazzola dreams of building bio-hybrid soft machines, which will perform computations using both synthetic and living tissues. The octopus was an excuse to develop this technology [for broader use], he said.

For example, soft robots may be especially well-suited for navigating harsh environments where standard rigid robots struggle, like these tube-like inflatable robots that can squeeze through tight spots and lift heavy objects. And softness is always the goal for robots that will interact with humans in potential medical, caretaking, or emergency response roles.

Its a vast space, Gazzola said, with a wide range of future applications to explore. If you understand the body, you can use it to solve an otherwise difficult problem in a simple fashion. This demonstrates that this is possible.

Reference: C Shih, et al., Hierarchical Control and Learning of a Foraging CyberOctopus, Advanced Intelligent Systems (2023). DOI: 10.1002/aisy.202300088

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Meet CyberOctopus, your guide to the world of machine learning ... - Advanced Science News