AI News, The Power, and Limits, of Artificial Intelligence

Artificial intelligence in business

Businesses are increasingly looking for ways to put artificial intelligence (AI) technologies to work to improve their productivity, profitability and business results.

Another key roadblock to AI adoption is the skills shortage and the availability of technical staff with the experience and training necessary to effectively deploy and operate AI solutions.

Research suggests experienced data scientists are in short supply as are other specialised data professionals skilled in machine learning, training good models, etc.

Software programs need regular upgrading to adapt to the changing business environment and, in case of breakdown, present a risk of losing code or important data.

These include: While you can't ignore these risks, it is worth keeping in mind that advances in AI can - for the most part - create better business and better lives for everyone.

Artificial Intelligence in Medicine: Applications, implications, and limitations

The accumulating data generated in clinics and stored in electronic medical records through common tests and medical imaging allows for more applications of artificial intelligence and high performance data-driven medicine.

Similar to how doctors are educated through years of medical schooling, doing assignments and practical exams, receiving grades, and learning from mistakes, AI algorithms also must learn how to do their jobs.

In order to generate an effective AI algorithm,  computer systems are first fed data which is typically structured, meaning that each data point has a label or annotation that is recognizable to the algorithm (Figure 1).

These algorithm “exams” generally involve the input of test data to which programmers already know the answers, allowing them to assess the algorithms ability to determine the correct answer.

For example, the actionable result could be the probability of having an arterial clot given heart rate and blood pressure data, or the labeling of an imaged tissue sample as cancerous or non-cancerous.

In the fall of 2018, researchers at Seoul National University Hospital and College of Medicine developed an AI algorithm called DLAD (Deep Learning based Automatic Detection) to analyze chest radiographs and detect abnormal cell growth, such as potential cancers (Figure 2).

The second of these algorithms comes from researchers at Google AI Healthcare, also in the fall of 2018, who created a learning algorithm, LYNA (Lymph Node Assistant), that analyzed histology slides stained tissue samples) to identify metastatic breast cancer tumors from lymph node biopsies.

Both LYNA and DLAD serve as prime examples of algorithms that complement physicians’ classifications of healthy and diseased samples by showing doctors salient features of images that should be studied more closely.

On top of that, the people creating algorithms to use in the clinic aren’t always the doctors that treat patients, thus in some cases, computationalists might need to learn more about medicine while clinicians might need to learn about the tasks a specific algorithm is or isn’t well suited to.

While AI can help with diagnosis and basic clinical tasks, it is hard to imagine automated brain surgeries, for example, where sometimes doctors have to change their approach on the fly once they see into the patient.

Either way, increasing transparency in the short term is necessary so that patient data is not mishandled or improperly classified, and so it could be easier to determine whether an algorithm will be sufficiently accurate in the clinic.

By establishing relationships between clinicians that understand the specifics of the clinical data and the computationalists creating the algorithms, it’ll be less likely for an algorithm to learn to make incorrect choices.

It might be necessary for companies to sacrifice the secrets of their algorithm’s functionality so that a more widespread audience can vet the methods and point out sources of error that could end up impacting patient care.

Defining the qualities necessary for an algorithm to be deemed sufficiently accurate for the clinic, while addressing the potential sources of error in the algorithm’s decision making, and being transparent about where an algorithm thrives and where it fails, could allow for public acceptance of algorithms to supplant doctors in certain tasks.

Artificial intelligence reinforces power and privilege

What do a Yemeni refugee in the queue for food aid, a checkout worker in a British supermarket and a depressed university student have in common?

Advanced nations and the world's biggest companies have thrown billions of dollars behind AI - a set of computing practices, including machine learning that collate masses of our data, analyse it, and use it to predict what we would do.

In our interview, he recalled years of debate with Minsky about whether AI was real or a myth: 'At one point, [Minsky] said to me, 'Look, whatever you think about this, just play along, because it gets us funding, this'll be great.' And it's true, you know ...

And if you went into the funders and you said, 'We're going to make these machines smarter than people some day and whoever isn't on that ride is going to get left behind and big time.

During President Barack Obama's drone wars, suspicion didn't even need to be personal - in a 'signature strike', it could be a nameless profile, generated by an algorithm, analysing where you went and who you talked to on your mobile phone.

Now a similar logic pervades the modern marketplace, the sense that total certainty and zero risk - that is, zero risk for the class of people Lanier describes as 'closest to the biggest computer' - is achievable and desirable.

Credit agencies and insurers want to build a better profile to understand whether you might get heart disease, or drop out of work, or fall behind on payments.

This originally meant that the skills and advantages of connected citizens in rich nations would massively outrun poorer citizens without computers and the Internet.

This drove policies like One Laptop Per Child - and it drives newer ones, like Digital ID, the aim to give everyone on Earth a unique identity, in the name of economic participation.

We can do better than to split society into those who can afford privacy and personal human assessment - and everyone else, who gets number-crunched, tagged, and sorted.

Unless we head off what Shoshana Zuboff calls 'the substitution of computation for politics' - where decisions are taken outside of a democratic contest, in the grey zone of prediction, scoring, and automation - we risk losing control over our values.

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