Recent incidents involving autonomous vehicle crashes, have made industry participants assume a lower profile. While these setbacks may slow progress in the sector, it does not mean the companies involved are taking their foot off the gas pedal in developing technology to move autonomous vehicles forward.

Any advanced technology industry will encounter growing pains, and as I said in my latest report, this space is just getting interesting. Allied Market Research puts the global autonomous vehicle market at $54 billion this year and $556 billion in 2026, growing at 40% per year. Even if these numbers are off by 25%, we’re still looking at a rapidly growing market.

And while the regulatory issues are real, there is growing economic pressure to solve the regulatory puzzle. Over 68% of freight travels on U.S. roads for an extended period of time. And with the trucking industry unable to fill driver positions, even with increasing pay and benefits, the American Trucking Association reports delays and costs are rising.

This lack of drivers, combined with an explosion in delivery of everything conceivable that consumers may purchase, as we recently looked at in our piece on the sharing economy, is placing growing pressure on regulators to formulate solutions to the autonomous vehicle problem. Trusting these issues will be resolved, let’s take a look at a few companies that are forging ahead in the autonomous vehicle space.

NXP Semiconductors (Nasdaq: NXPI)

Netherlands based NXP Semiconductors is the world’s largest supplier of automotive semiconductors, and will have a major role to play as cars and trucks move to autonomy. One of the things I like about NXP is the fact that it is already deeply embedded in the auto industry. This gives the company insight into customer needs as autonomous vehicles move through the Levels of Autonomy I detailed in my previous article.

NXP is aggressively expanding its relevance in autonomous vehicles, both through internal development of products as well as through acquisitions. Late last year the company acquired OmniPHY, a pioneer in high-speed automotive Ethernet IP. Through the acquisition, NXP is ensuring it can maintain the speed and number of connections necessary as vehicles become increasingly autonomous.

As Ian Riches, Executive Director of Strategy Analytics Global Automotive Practice puts it, “One of the vexing questions of the Autonomous Age is how to move data around the car as fast as possible. Cameras and displays will ramp the number of high-speed links in the car to 150 million by 2020 and by 2030 autonomous car systems will aggressively drive that number to 1.1 billion high-speed links.”

By purchasing OmniPHY, NXP is enhancing its capability to capture the entire value chain in the connected automobile. Controlling connectivity points is a major strategic competitive advantage, and allows NXP to work not only with automakers, but with other equipment and sensor companies, cementing NXP as a vital player in the autonomous vehicle ecosystem.

From a valuation perspective, NXP trades at a 13 PE ratio, and pays just over a 1% dividend. The company has grown earnings per share an average of 62% over the past 5 years, and currently has profit margins of over 28%. NXP shares have never fully recovered from a failed merger attempt with Qualcomm (Nasdaq: QCOM) in 2018 after a prolonged, almost 2 year courtship. The deal fell through and many NXP stock owners, who had been in the stock only for the merger, abandoned ship. The stock appears to have bottomed around $70, and now trades close to $80, well off the pre-merger prices in the $120s.

A combination of value and strategic market positioning makes NXP a buy at these levels. The company has ensured its relevance in the autonomous vehicle market and should reap the benefits as this market matures.

Magna International (NYSE: MGA)

Magna International is a Canadian company that wants to ensure the autonomous vehicle is not only safe and secure, but retains the style and design each automotive manufacturer has heavily invested in. As Magna explains, it does not want the autonomous car to look like a science experiment with vehicle sensors, such as LIDAR, mounted obtrusively all over the car.

Using what it terms MAX4, Magna has developed a self-driving system that can enable up to Level 4 autonomous capabilities, while at the same time disguising the fact that the automobile is anything different than what the manufacturer has on the showroom floor today. The MAX4 system can be mounted on an automobile, such as a Jeep Grand Cherokee, with the LiDAR, radar, and ultrasonic sensors all contained within the bumpers and other currently existing cavities of the car.

While I’m not a huge fan of the company, the Tesla (Nasdaq: TSLA) business model does demonstrate consumers want not only the latest technology, they want functionality wrapped in design. And they will pay premium prices for this combination. Magna ensures that each automaker can retain and enhance vehicle look and design, without concern that the sensor requirements of the autonomous vehicle impinge on their branded look.

And, Magna is not only offering the ability to retain design, the company is also forward thinking in addressing the changing needs, and look and feel, of the interior of future autonomous vehicles. The company recently released its flexible seat configuration design for autonomous vehicles.

The seating system allows for three variations in seating patterns that are fully automatic, with seats moving electrically along tracks mounted in the vehicle floor. Consumers have the option of a “campfire” seating arrangement with all seats facing the middle of the vehicle, a “cargo” configuration where seats move to the front of the car to provide a large cargo area in back, and a seating configuration with electronically insulated sound barriers which allows for private phone conversations in a ride sharing environment. Magna is closer to the ride share business than other companies, with a $200 million private investment in sharing economy company Lyft (Nasdaq: LYFT, pre-IPO).

Magna currently trades with a PE of 7 and pays just over 2.5% in dividends each year. The company has averaged 14% earnings growth over the past 5 years, and is projected to grow earnings 13% this year. As with NXP, a pullback in the stock in 2018 is providing an excellent entry price at these levels. Magna’s innovative designs and technology should provide earnings expansion as the company outfits the car of the future.

Aptiv (NYSE: APTV)

Finally, I would like to revisit a portfolio holding of the Growth Stock Advisor service, Aptiv. Aptiv is an Ireland based company that was spun out of Delphi Technologies (NYSE: DLPH), formerly Delphi Automotive, in late 2017.

Delphi Technologies is now the “powertrain” part of the business providing propulsion, combustion and electronic solutions. This is the old industrial commodity side of the business, and the stock has performed abysmally since the spinoff of Aptiv.

Aptiv is, as former Delphi Automotive, and now Aptiv CEO Kevin Clark stated, focused on “active safety, autonomous driving, enhanced user experiences, and connectivity”. Aptiv provides the sensors, connectivity, and most importantly deep industry and product expertise, which is so important to establishing credibility and trust from automotive manufacturers moving into the autonomous vehicle space.

As I detailed in my latest report on autonomous vehicles, there are hurdles to full autonomy, one of which is working out the legal liability issues that arise from a fully autonomous vehicle. But, Aptiv is making the incremental improvements that ensure the autonomous vehicle will be ready when the regulatory issues are resolved.

After getting the lowdown at CES on the Aptiv fleet of Lyft cars that is ferrying passengers around Las Vegas, Extreme Tech put the Aptiv advancements over last year this way, “For example, implementing RTK (Real-Time Kinematic GPS augmentation) has allowed the cars to locate themselves within 2.5 cm (instead of 10 cm). That makes the difference between not knowing and knowing whether a pedestrian is standing on the edge of the curb or in the crosswalk.” Advancements like these are crucial to the developing regulatory framework, and to putting fleets of autonomous vehicles on every road, not just in test environments like that taking place in Las Vegas.

Aptiv is continually advancing technology, and with deep roots in the automotive industry, it is one of the purest autonomous vehicle plays available to investors. The company has a forward PE of 13, and is expected to grow earnings this year 31%.

Each of these companies offers a great way to play advancements in the autonomous vehicle industry as it matures in the coming years. Whether through the brains and connectivity of NXP, the technology and design of Magna, or the autonomous integration provided by Aptiv, it’s hard to go wrong with these top players in the space.

Source: Investors Alley

The main takeaway I got from the annual Consumer Electronics Show (CES) in Las Vegas was that enthusiasm for autonomous (driverless) vehicles among the world’s auto makers has really cooled.

Just a year or two ago, car and some tech companies could hardly restrain their excitement over the next revolution in mobility – driverless vehicles. But now, as the next step in driving automation comes closer to reality, some auto industry executives seem keen to back away from implementation and move directly on to the next step. Let me explain…

Levels of Vehicle Autonomy

First, let me fill you in on the levels of autonomy for a vehicle as set by the standards organization, the Society of Automotive Engineers International. There are six levels of autonomy, from zero for absolutely no autonomy to Level 5, which would be complete autonomy. In other words, completely controlled by computers. This technology is probably a decade away.

Here are brief descriptions of the other levels of autonomy:

Level One: Driver Assistance: At this level, the automobile includes some built-in capabilities to operate the vehicle. The vehicle may assist the driver with tasks like steering, braking or acceleration. For several years now cars have been manufactured with controls on the steering column that allow the driver to maintain a constant speed or gradually increase or decrease speed. These functions are enacted by the driver and not automatically performed by the automobile. Most modern cars fit into this level. If your vehicle has adaptive cruise control or lane-keeping technology, it’s probably at level one.

Level Two: Partial Automation: At this level of automation, two or more automated functions work together to relieve the driver of control. An example is a system with both adaptive cruise control and automatic emergency braking. This is referred to as an advanced driver assistance system (ADAS). Examples of level two include Tesla Autopilot, the Mercedes-Benz Distronic Plus and the General Motors Super Cruise.

Level Three: Conditional Automation: This level is marked by both the execution of steering and acceleration/deceleration and the monitoring of the driving environment. In levels zero through two, the driver does all the monitoring. At level three, the driver is still required, but the automobile can perform all aspects of the driving task under some circumstances. Levels three and higher qualify as automated driving systems (ADS). There’s a big jump in capability between levels two and three. The driver still has to keep his eyes on the road, ready to take over at a moment’s notice. But a level three vehicle can handle certain parts of the trip on its own – mainly highway driving.

Level Four: High Automation: Level four vehicles don’t need a human driver. The vehicle can do all the driving, but the driver can intervene and take control as needed. This level of automation means that the car can perform all driving functions “under certain conditions.” The test vehicles currently on the road would fall under this category. Google’s Waymo is testing level four vehicles.

The Level Three Barrier

However, many auto companies are reluctant to even move to Level 3 autonomy. And it’s easy to see why – that’s the first point at which full responsibility — and legal liability — shifts from the driver to the car. Both automakers and regulators are wary about whether transferring control between car and driver can work effectively in an emergency.

That could lead to a legal nightmare when accidents do occur. And it’s why Audi has never turned on its Level 3 software on its vehicles sold here in the U.S.

The idea of a driverless car that can hand back control to a human with little warning has always divided the auto industry. Carmakers such as Toyota, Volvo and Ford, as well as Waymo have always been skeptical about the idea of Level 3 vehicles. These companies believe it is safer to wait longer for more advanced forms of automation that require no human intervention.

Daimler Trucks, the world’s biggest maker of commercial vehicles, also turned its back on Level 3 recently. It said that the technology sent a confusing message to drivers: they are encouraged to switch their attention to something other than the road, but expected to be ready to retake control at a moment’s notice. Other car companies, such as BMW, are moving ahead – its iNext vehicle in 2021 will feature Level 3 technology enabling hands-free, pedal-free driving.

Driverless Vehicle Investments

So what companies seem best poised to benefit from the move toward greater autonomy for vehicles?

I do like Toyota Motors (NYSE: TM) because it seems to be moving on its own independent path, separate from the other automakers with regard to new technologies. I like their skepticism toward Level 3 vehicles.

And their skepticism toward electric vehicles is interesting. Instead, it is concentrating its efforts on both solid state batteries and fuel cell vehicles. I will be discussing Toyota’s and Japan’s move toward a hydrogen economy in a future article.

I find solid state batteries fascinating. They are capable of holding more electricity and recharging more quickly than their lithium-ion counterparts. These batteries could do to lithium-ion power cells what transistors did to vacuum tubes: render them obsolete.

As the name implies, solid-state batteries use solid rather than liquid materials as an electrolyte. That is the stuff through which ions pass as they move between the poles of a battery as it is charged and discharged. Because they do not leak or give off flammable vapor, as lithium-ion batteries are prone to, solid-state batteries are safer (lower fire hazard). They are also more energy-dense (leading to higher power capacity) and thus more compact. Solid-state batteries are also a promising power source for internet-of-things devices that are coming into wider usage daily.

I also like Daimler AG (OTC: DMLRY). Its Freightliner Cascadia will go on sale this year and be the first truck in North America to feature partially-automated driver assistance.

And Daimler is heading straight from level two to level four, in which a truck can operate without user intervention on specific routes because the company says level three “does not offer truck customers a substantial advantage”. Unlike Tesla, it considers Level 3 to be a dead end since it would have to rely on human attention during the crucial 1% of the time after telling the driver not to pay attention 99% of the time. With truckers on long haul routes, I see sleep and Level 3 not being compatible.

Finally, if I must go with a company that is pursuing Level 3 automation, I’ll go with General Motors (NYSE: GM) instead of the cult stock known as Tesla.

Its Level 3 system is better than Tesla’s. If the driver still doesn’t take control after several prompts, the system will gradually bring the vehicle to a complete stop, activate the hazard warning flashers, and call for help (using GM’s OnStar system.) GM also built a slew of additional safeguards into Super Cruise to try to ensure that it’s only used in circumstances it can safely handle. For example, if the vehicle isn’t on a highway, the road’s lane markings aren’t clearly visible, or the system thinks that the driver isn’t fully attentive – it won’t even switch on.

And GM is challenging Tesla in the electric car space. It is re-casting its luxury Cadillac brand as an electric brand. GM says the premium marque would be the company’s “lead electric vehicle brand and will introduce the first model from the company’s all-new battery electric vehicle architecture”. Tesla has long dominated sales of premium electric vehicles, but it faces fresh challenges, not only from the upcoming electric Cadillac, but also from European premium nameplates.

GM is making a lot of right moves, from cost cutting to getting major investments from Honda and Softbank. That led it to recently forecast higher than expected 2018 profits. It also said earnings will rise further in 2019, despite flat or declining car sales in both the U.S. and China, its two primary profit drivers. The automaker expects adjusted earnings per share for 2018 to exceed the guidance given in October of $5.80 to $6.20, and 2019 EPS to rise to $6.50 to $7.00 per share, above market estimates.

Stay tuned to this space though, the race for autonomous vehicles is just getting interesting.

Source: Investors Alley

This time last year, I predicted that Amazon’s (NASDAQ:AMZN) business would continue to grow at a rapid pace, pushing AMZN stock all the way to $10,000.

Of course, I wasn’t suggesting it would happen immediately. Rather, it might take as long as 7.5 years to get there, which would still result in an annualized return of 33% for Jeff Bezos and company.

“In my estimation, if it took 7.5 years to get to $1,000 from $100, I see AMZN stock getting to $10,000 somewhere between January 2023 and January 2025.“

In September, the markets went into full-on bear mode, knocking the S&P 500 for a 14% loss, making it the worst quarter for the index since 2008, and in the process, putting it into negative territory (-7%) in 2018. Talk about a reversal of fortune.

How did AMZN stock fare in the fourth quarter? It lost 25% to close well down from its 52-week high of $2,050.50. No matter. Early in 2019, Amazon’s managed to regain some of those losses — it’s up 7.8% year-to-date through the Jan. 9 close.

Like a young child taking learning to walk, step by step, Amazon will get back on the path to $10,000. In fact, it needs only a couple of its big initiatives to be successful to reach the lofty goal sooner rather than later.

Here are two of them:

Amazon’s Push Into Advertising

When investors name-drop companies in online advertising, two names come to mind: Facebook (NASDAQ:FB) and Google, part of Alphabet (NASDAQ:GOOGL, NASDAQ:GOOG).

Facebook and Google account for 58% of the U.S. online advertising market. Together, the two firms generated approximately $61 billion in ad revenue in 2018. Way down in third place with 7%market share of U.S. digital ad spending is Amazon — but it’s coming on like a house on fire. Forecasts suggest that Amazon ad revenues could hit $38 billion annually by 2023.

While Amazon has little chance of catching the duopoly that is Facebook and Google, it stands a good chance of becoming a strong third wheel.

“My understanding from speaking with people in the industry is that Amazon’s retail, subscription-based and advertising revenues are fairly fluid,” said Pivotal Research senior analyst Brian Wieser. “Amazon will optimize revenue streams and profitability based on what it sees from consumers.”

So while Facebook and Google are heavily dependent on advertising, Amazon has stuff like Amazon Web Services (AWS) — $5.1 billion in operating income in the first nine months of its latest fiscal year — that are highly profitable to lean on while building some of its other businesses such as advertising.

Jeff Bezos has become a master allocator of capital, stoking the fires that need stoking, and in the process, making Amazon extremely nimble and able to act on its best ideas.

Amazon Go

The cashier-less convenience store concept Amazon began rolling out in 2018 is going to revolutionize the industry. Amazon, which expects to open 3,000 stores by the end of 2021, could generate as much as $4.5 billion in annual sales from these stores in three years’ time. According to RBC Capital Markets, the average Amazon Go store does $1.5 million in annual sales.

Not bad for a store concept that doesn’t need any front-of-store personnel.

“Amazon Go stores could be a game changer for physical retail experience. Its in-store technology enables shoppers to have a very efficient and pleasant shopping experience,” RBC analyst Mark Mahaney wrote in a note to clients. “While not a significant financial contributor yet, we believe the overall opportunity is huge.”

Darn straight.

The convenience store industry hasn’t changed in 25 years. Those aren’t my words — they’re Alimentation Couche-Tard (OTCMKTS:ANCUF) CEO Brian Hannasch’s.

“The experience of buying fuel and of buying items in our stores has largely been unchanged since card readers were introduced 25 years ago,” Hannasch said in a November story in ConvenienceStore News. 

The convenience store business might not be as sexy as Whole Foods, but it’s got the potential to engage Amazon Prime customers wherever they live.

The stores themselves might generate $4 billion in sales, but it’s the free advertising Amazon gets to attract more Prime members that’s the real key to Amazon Go’s ultimate success.

Everything Amazon does revolves around Prime. 

The Bottom Line on AMZN Stock

Nothing Amazon does surprises me anymore. It’s got a great business model and is using technology to innovate old-school industries like grocery, convenience stores, healthcare — the list goes on. I could actually see Amazon stock hit $10,000 within five years, two-and-half years sooner than I suggested last January. However, it’s got to nail at least one of these initiatives to have a chance.

As of this writing, Will Ashworth did not hold a position in any of the aforementioned securities.

Semiconductor stocks are typically the forward indicators for technology stocks. So when Micron Technologies (NASDAQ: MU) confounded the markets with its consistent single-digit price-to-earnings ratio last year, investors should have exercised caution. The good news is that the patient investor may wait out the cyclical downturn in the chip sector. It will take another six to nine months before reaching a demand and supply equilibrium. Within the graphics chip space, Nvidia (NASDAQ: NVDA) has the clearest headwinds to work through. In knowing what inventory it needs to work through, NVDA stock could start recovering within one or two quarters.

Nvidia experienced excess inventory in the channels, which hurt its revenue forecasts. It blamed the crypto hangover for the excess supply imbalance. At significantly lower prices, cryptocurrency is not likely to recover any time soon. This is the bad news for anyone holding crypto. For NVDA and Advanced Micro Devices (NASDAQ:AMD) shareholders, chances are good that the excess GPUs on the market will clear.

The two firms likely benefited from an increase in sales during the holiday season, after retailers offered rebates and discounts on graphics cards. Once the last-generation card supplies are cleared, game developers will embrace Nvidia’s ray-tracing. RTX card sales slumped in the last quarter and will be weak again for the next two quarters due to cheaper models still on the market.

The fact that Electronic Arts (NASDAQ:EA) was the only big name embracing RTX through its Battlefield 5 title did not help RTX sales. Worsening Nvidia’s near-term prospects was the significant drop in sales of BF V.

Market Opportunity for Ray-Tracing

Nvidia’s Turing brought ray-tracing to games, but pushes its technology forward through the Pro Visualization business. For the last 10 years, the industry simulated such effects as the reflection of light and rays of light bouncing off objects. Ray-tracing processes these effects in real-time. In performance terms, it should bring a 25% – 30% improvement over the Pascal architecture. And at the top-end, performance is 10-fold better.

Within the enterprise space, such as TV, film and Photoshop work, RTX will speed up the development of special effects. Nvidia inserted its graphics technology in around 1.5 million servers, which is worth a few billion dollars in business revenue. As companies slowly embrace RTX, investors should expect the company maintaining and even growing its profit margin.

At a $133 share price, NVDA stock is trading at a more reasonable multiple of around 19 times earnings. With earnings-per-share growth of 15.5% over the next five years, the stock is valued at a PEG of 1.20 times and 19 times forward earnings. AMD, despite falling to $18, still trades at a 30 times P/E multiple.

Fair Value

Analysts did not yet lower their price target on Nvidia. At a $228.50 average price target, based on 30 analysts, the 76% upside (per Tipranks) appears out of touch.

Source: tipranks

As shown in the table above, only one analyst from RBC Capital posted a report on Nvidia stock. The other analysts did not change their view in the last month.

NVDA Is Too Cheap to Ignore

At a P/E now in the teens, markets severely punished Nvidia for failing to forecast GPU demand.

The selloff, which started in October, is now over-done. When the company reports results in February, it will have a better idea on RTX sales for the year, along with the progress slimming down Polaris inventory.

Investors may consider starting a position in NVDA stock at these levels.

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Despite the recent announcement from drug giant Pfizer that it was raising prices by an average of 5% on about 10% of its drugs on January 15, the warning signs for the industry’s profitability are becoming more frequent.

The latest drug pricing proposal from Senator Bernie Sanders had some similarities to one from the Trump Administration. Down the road, that may lead to a compromise and one of the few areas Democrats and Republicans can agree on – restraining the steady rise in drug prices. And it’s easy to see why – in 2016, total U.S. prescription drug expenditures were estimated at $450 billion!

Big Pharma’s Dilemma

This leaves the large pharmaceutical companies in a dilemma. One the one side is the government trying to restrain prices while on the other side is the hard, cold fact that research and development of new drugs is growing more costly with the returns on this R&D shrinking every year.

On average, it takes about 12 years of research and development and an expenditure of $2.6 billion to move an experimental new drug from the laboratory to the market. Yet, their business is to find new drugs to treat patients.

Keep in mind that the most recent data from the CDC shows that 48.9% of Americans have a chronic condition that requires them taking at least one prescription drug within the last 30 days. I have to take three meds daily and I’m sure you, or someone in your immediate family, has a need for a prescription medicine.

A study centered on the 12 biggest pharmaceutical firms from Deliotte highlighted the recent negative R&D trend for these companies. Here are a few of the highlights of that study:

• R&D returns have declined to 3.2%, down from 10.1% in 2010
• Projected peak sales per asset decreased by more than half between 2010 and 2016
• The uptick in costs and sales per asset in 2017 was due to the drop in the number of assets in late-stage pipelines—from 189 in 2016 down to 159 in 2017.

So what can these companies do to turn their fortunes around?

One solution lies in emerging technologies such as robotics, big data and artificial intelligence (AI). Both drug companies and the technology giants are investing billions of dollars into AI, hoping it will make the drug discovery process both faster and cheaper.

AI and Pharma Research

In February 2018, Eric Horvitz – director of Microsoft Research Labs told the annual meeting of the American Association of the Advancement for Science – “I believe that AI is a sleeping giant for healthcare in general.” He said Microsoft was investing in AI for drug design and pharmacology, which studies how drugs act in the body, and called the technology a “tremendous opportunity.”

And Microsoft is far from alone in its AI bet. The Toronto-based biotech company BenchSci says more than 16 pharma firms and over 60 startups were using AI for drug discovery.

The biggest bottlenecks in drug development usually occur in the very early stages of research. That involves the the time needed in going from identifying a potential disease target (often a protein within the body) to testing whether a company’s drug candidate can hit that target. The most ambitious AI efforts aim to compress that process — which can take four to six years — into just one year.

2018 has been a banner year, with a huge jump in investment from big pharma, often in conjunction with health tech groups, into using AI. The research firm Deep Knowledge Analytics says at least 15 firms have integrated AI into their drug discovery process.

The major pharma firms are taking varied approaches – some are partnering with health tech groups, some are keeping everything in-house, and some are following both paths.

Some the major pharma firms involved include: GlaxoSmithKline with Exscientia, AstraZeneca and Sanofi with Berg Health and Merck with Numerate. Some companies though are working with AI in-house. Positive developments here include Pfizer using AI to mine patient data — stored anonymously in electronic medical records — for signs of a rare form of heart failure. And Novartis hopes a drug partially developed using AI will be registered within the next 36 months.

I believe using AI in drug discovery will be quite commonplace by the middle of the next decade. So what companies will be leading the way? Let me give you a couple of the top candidates among the major pharmaceutical companies.

Two Companies Leading the Way

GlaxoSmithKline (NYSE: GSK) is probably the most active of all pharmaceutical companies in applying artificial intelligence to drug discovery. It even created an in-house artificial intelligence unit called the “In silico Drug Discovery Unit.”

GSK has partnered with startups including Exscientia and Insilico Medicine.The partnership with Excscientia, announced in July 2017, has a goal of discovering novel small molecules for up to 10 disease-related targets across several therapeutic areas. The partnership with Insilico, announced in August 2017, is to identify novel biological targets and pathways.

The company is also part of the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, which aims to leverage artificial intelligence to go from drug target to patient-ready therapy in less than a year. GSK gave ATOM some very useful ‘big data’ – chemical and in vitro biological data for more than two million compounds it has screened.

Other notable efforts in the AI space from GSK include the announcement in May 2018 of a partnership with Cloud Pharmaceuticals to use AI for the design of novel small molecule drugs. And the company is also working with Googleon applying AI to drug discovery. Researchers from the two firms have already developed a machine learning algorithm to identify protein crystals.

Another of my favorites in the sector with regard to the use of AI is the Swiss drug giant, Roche Holding Ltd. (OTC: RHHBY). This company’s stock, in the form of an ADR that trades well over 1.2 million shares a day, has been a slow and steady climber. It is very near its 52-week high despite a terrible stock market background.

In June 2017, Genentech (a Roche subsidiary) announced a collaboration with the precision medicine firm GNS Healthcare focused on cancer therapy. The companies aim to use machine learning to convert high volumes of cancer patient data into computer models that can be used to identify novel targets for cancer therapy. Among the investors into GNS Healthcare are Amgen and Celgene.

Back in December 2014, Roche acquired Bina Technologies, a biotech company targeting the personalized medicine sector by providing a platform for large-scale genome sequencing. In its description of services, Bina Technologies has machine learning experts as part of its team.

Finally, Roche is another of the handful of pharmaceutical partners for the aforementioned Boston-based Berg Health, a company focused on applying AI in for the discovery and development of drugs, as well as diagnostics and healthcare applications.

High attrition rates among drug candidates are one of the main reasons for expensive drug prices, as pharma companies look to offset the cost of failed projects against the very few successful ones. Improving productivity though AI holds out hope that the attrition rate will drop, lowering drug prices.

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It's not gold, crypto or any mainstream investment. But these mega-billionaires have bet the farm it's about to be the most valuable asset on Earth. Wall Street and the financial media have no clue what's about to happen...And if you act fast, you could earn as much as 2,524% before the year is up.
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Source: Investors Alley 

The Turing Test, which is considered one of the best ways to identify artificial intelligence (AI), requires that a human being is unable to distinguish a computer from another human being when asking both of them the same questions. It’s a great academic exercise, but not especially helpful in determining which company in the AI area you should invest in.

When it comes to predicting the size of the the “AI market” we find projections for market size in 2025 from well-respected firms ranging anywhere from $37 billion to $1.2 trillion. A large part of the deviation in estimates is due to the lack of clarity in defining AI, and how the technology will benefit companies able to employ AI in their business models.

As Eliezer Yudkowsky, an American writer who has warned on the danger of AI laments, “By far, the greatest danger of AI is that people conclude too early that they understand it.” So here we stand. We don’t know exactly what AI is, we have difficulty defining the market, and we don’t know what the future capabilities of the technology look like.

A Forbes article earlier this month by Kathleen Walch, was actually titled Artificial Intelligence is Not a Technology. Ms. Walch, an AI expert, considers AI to be a “journey” with technology spun off along the way.

While we may have many questions about what AI is, we do know that the two leading countries in terms of AI research are China and the U.S. China has published more research papers on AI, and the government announced last year the country would become, “a principal world center of artificial intelligence innovation.” But, some question the quality of China’s research and most believe the U.S. has a slight lead in the technology.

We also know that many companies are attacking big problems using the current state of AI, and that the technology will have a major impact on our everyday lives. For example, Deere (NYSE: DE) recently bought a company called Blue River Technology. The company uses robots and AI to fertilize, water, and harvest crops. The result is the use of less fertilizer, less water, and better yielding crops. In this way, AI is already being used to prevent pollution, conserve water, and feed more people.

As investors we should also know that one form of AI, machine learning, is already being used by many of the largest companies on Wall Street. Andrew Ng, Stanford professor and founding lead of Google Brain defines machine learning as “the science of getting computers to act without being explicitly programmed.”

Let’s go a just a little further down the machine learning rabbit hole in order to understand where companies plug into the machine learning ecosystem. Machine learning can be broken down into two stages. First, there is the “training” of the computer. This involves providing patterns, e.g. videos, pictures, and any other of a wide variety of data, to the computer and asking it what the pattern is. The computer is allowed to get the answer wrong over and over, until eventually getting it right. In this way the computer “learns” what information presented in a specific manner means to humans.

The second stage is “inference”. In this stage the computer takes what it has learned in the training stage and “infers” an answer based on the learning that has taken place. Inferring involves massive amounts of data and must often be done in a split second.

Think of the inference stage in a driverless car. While traveling 55 MPH, surrounded by other cars, a driverless car “sees” a child’s ball roll into the road ahead. The car must simultaneously recognize the fact that it is a child’s ball, determine there is a high probability a child may follow the ball into the street, communicate with the other driverless cars around it what it sees, and then decide whether the best course of action is to brake, change lanes, or take some other evasive action.

Clearly the inference must be fast and correct. One company working to insure inferences are arrived at in a timely manner is:

Xilinx (Nasdaq: XLNX)

Xilinx is known today as the field programmable gate array (FPGA) company. They compete with companies like NVIDIA (Nasdaq: NVDA) that make graphics processing units (GPU). Demand for NVIDIA’s GPUs, driven by video gaming, data centers, cryptocoin mining, and AI, has driven the company’s stock up over 788% from the beginning of 2016 to highs reached earlier this year.

While FPGAs and GPUs both process data, FPGAs are generally faster and use less power, but GPUs are cheaper. When comparing the two options, chiefly cost constraints, in both crypto coin mining and video gaming platforms, has historically GPUs the technology of choice, and in the process powered NVIDIA’s stock skyward.

GPUs are also favored for the “training” stage of machine learning, because speed is not a vital component of training as it is for inference.

But, I believe that growth in the inference market, as a variety of technologies come online in the next few years e.g. 5G, IoT, and driverless automobiles, will concurrently drive sales of Xilinx FPGAs. XLNX grew earnings in its latest reported quarter by 25.37% year-over-year, and is expected to grow full year earnings 16.08%.

The company also recently introduced of a new technology, which is one reason I believe now is a good time to get into the stock. The new chip from Xilinx, which combines FPGA, GPU and CPU capabilities on one chip, may be its ace in the hole.

In October Xilinx CEO Victor Peng introduced a new chip aimed squarely at the inference market. The Adaptive Compute Acceleration Platform (ACAP) chip is code named Everest, and according to Xilinx the chip can “infer” 2x-8x faster than Nvidia’s GPUs, and do so with 4x less energy. The Everest chip is featured in the company’s Versal line of products, short for “versatile” and “universal”.

Mr. Peng took over the CEO position at Xilinx in January after joining the company in 2008 and most recently served as Chief Operating Officer. With the launch of the new more powerful technology, Mr. Peng is already attempting to leap ahead of the competition by changing the company’s branding.

“…we have to say no, we’re not the FPGA company. With ACAP, at the moment nobody even knows what that is – but they will understand over time.” The combination of rising demand for their FPGA product with an aggressive move to expand the new ACAP technology, should put Xilinx in the sweet spot of a coming machine learning boom.

Alteryx (NYSE: AYX)

While Xilinx makes the hardware necessary for the inference stage, Alteryx uses machine learning to provide the end user with the ability to gather the inference and make it actionable. Alteryx is a “self-service data analytics” company. They provide data processing and presentation software allowing companies to turn data into human consumable presentations and graphs which can be used to make business management decisions.

The Alteryx solution allows customers to combine data from a variety of sources, e.g. warehouse data, combined with customer data, combined with purchasing data, to present a holistic and repeatable picture of business health. The software, which includes various aspects of machine learning, provides insights without the user having to use or know computer code. Easily combining data from a variety of databases, and being able to present a coherent representation of that data, is a major boon to large corporations.

In its recently reported Q3 earnings, CEO Dean Stoecker reported the company grew revenue 59% year-over-year. But, more importantly, sustained net revenue retention, a measure of customers staying with the company and purchasing additional services, was 131%. The company has also grown earnings by over 46% year-over-year as of its latest quarterly report. Analysts are projecting a 5 year average earnings growth rate of 8% going forward.

This marked the eighth consecutive quarter the customer retention number was over 130%. This is important as it shows me that Alteryx is not just good at marketing, but actually has a product that customers want and are using more and more of. Having been able to continuously obtain new customers, while increasingly monetizing current customers, is one of the reasons I believe the stock is a buy here.

Another thing I like about the company’s growth is that they are diversifying their customer base both across industries and across geographies. Their clients now include Cowen and Company, J.Crew, Cisco Systems, McDonald’s and Textron, as well as AkzoNobel Sourcing in the Netherlands, Anheuser in Belgium, and Oxford University Press in the U.K.

As CEO Stoecker stated in the latest earnings call, using Alteryx allows customers to realize “significant time savings and reduced expenses…” An economic slowdown could actually increase demand for the company’s services as cost cutting comes back into vogue.

While Xilinx and Alteryx are already benefiting from the AI revolution, both companies appear poised to accelerate their growth. Whether it be through the introduction of new technology, or the ability to grow and retain their customer base, as the AI technology “journey” progresses to the next stage these companies should be highly considered for your AI portfolio allocation.

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Source: Investors Alley